magnet:?xt=urn:btih:470B1FF5490A3567307A728DB3FDC0360F484FD8
1.1 - How to Learn from Appliedaicourse/1.1 - How to Learn from Appliedaicourse.mp4 465.1 MB
34.2 - Productionization and deployment of Machine Learning Models/34.2 - Productionization and deployment of Machine Learning Models.mp4.mkv 280.3 MB1.2 - How the Job Guarantee program works/1.2 - How the Job Guarantee program works.mp4 255.7 MB5.1 - Numpy Introduction/5.1 - Numpy Introduction.mp4 164.7 MB5.2 - Numerical operations on Numpy/5.2 - Numerical operations on Numpy.mp4 163.6 MB45.9 - Univariate AnalysisGene feature/45.9 - Univariate AnalysisGene feature.mp4 151.2 MB3.1 - Lists/3.1 - Lists.mp4 148.1 MB49.6 - Softmax Classifier on MNIST dataset/49.6 - Softmax Classifier on MNIST dataset..mp4 146.9 MB57.26 - Data Control Language GRANT, REVOKE/57.26 - Data Control Language GRANT, REVOKE.mp4 145.4 MB51.6 - LSTM/51.6 - LSTM..mp4 143.8 MB54.4 - Char-RNN with abc-notation Data preparation/54.4 - Char-RNN with abc-notation Data preparation..mp4 138.1 MB41.9 - EDA Advanced Feature Extraction/41.9 - EDA Advanced Feature Extraction.mp4 137.7 MB51.10 - Code example IMDB Sentiment classification/51.10 - Code example IMDB Sentiment classification.mp4 128.7 MB23.5 - Naive Bayes algorithm/23.5 - Naive Bayes algorithm.mp4 122.4 MB42.13 - Code for bag of words based product similarity/42.13 - Code for bag of words based product similarity.mp4 122.0 MB50.2 - ConvolutionEdge Detection on images/50.2 - ConvolutionEdge Detection on images..mp4 121.6 MB23.6 - Toy example Train and test stages/23.6 - Toy example Train and test stages.mp4 121.5 MB45.13 - Baseline Model Naive Bayes/45.13 - Baseline Model Naive Bayes.mp4 121.0 MB53.12 - Test and visualize the output/53.12 - Test and visualize the output..mp4 119.3 MB17.1 - Dataset overview Amazon Fine Food reviews(EDA)/17.1 - Dataset overview Amazon Fine Food reviews(EDA).mp4 116.4 MB50.14 - Residual Network/50.14 - Residual Network..mp4 113.8 MB24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV.mp4 112.1 MB51.2 - Recurrent Neural Network/51.2 - Recurrent Neural Network..mp4 110.3 MB53.10 - NVIDIA’s end to end CNN model/53.10 - NVIDIA’s end to end CNN model..mp4 108.6 MB47.8 - Training an MLP Chain Rule/47.8 - Training an MLP Chain Rule.mp4 107.0 MB48.3 - Rectified Linear Units (ReLU)/48.3 - Rectified Linear Units (ReLU)..mp4 107.0 MB11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/11.9 - Q-Q plotHow to test if a random variable is normally distributed or not.mp4 106.6 MB48.18 - Auto Encoders/48.18 - Auto Encoders..mp4 102.3 MB4.2 - Types of functions/4.2 - Types of functions.mp4 100.7 MB18.27 - LSH for cosine similarity/18.27 - LSH for cosine similarity.mp4 100.7 MB18.30 - Code SampleDecision boundary/18.30 - Code SampleDecision boundary ..mp4 100.2 MB20.17 - curse of dimensionality/20.17 - curse of dimensionality.mp4 99.6 MB49.8 - Model 1 Sigmoid activation/49.8 - Model 1 Sigmoid activation.mp4 99.6 MB42.6 - Data cleaning and understandingMissing data in various features/42.6 - Data cleaning and understandingMissing data in various features.mp4 99.4 MB4.8 - File Handling/4.8 - File Handling.mp4 97.4 MB32.16 - Stacking models/32.16 - Stacking models.mp4 97.4 MB36.3 - Proximity methods Advantages and Limitations/36.3 - Proximity methods Advantages and Limitations..mp4 96.3 MB57.20 - Sub QueriesNested QueriesInner Queries/57.20 - Sub QueriesNested QueriesInner Queries.mp4 94.9 MB7.3 - Key Operations on Data Frames/7.3 - Key Operations on Data Frames.mp4 94.8 MB24.2 - Sigmoid function Squashing/24.2 - Sigmoid function Squashing.mp4 94.5 MB57.13 - Logical Operators/57.13 - Logical Operators.mp4 92.6 MB17.5 - Text Preprocessing Stemming/Stop-word removal, Tokenization, Lemmatization (Featurizations - convert text to numeric vectors).mp4 92.5 MB54.3 - Char-RNN with abc-notation Char-RNN model/54.3 - Char-RNN with abc-notation Char-RNN model.mp4 91.1 MB20.11 - Local outlier Factor(A)/20.11 - Local outlier Factor(A).mp4 91.0 MB49.12 - MNIST classification in Keras/49.12 - MNIST classification in Keras..mp4 90.9 MB48.16 - Softmax and Cross-entropy for multi-class classification/48.16 - Softmax and Cross-entropy for multi-class classification..mp4 90.1 MB14.9 - PCA Code example/14.9 - PCA Code example.mp4 89.6 MB48.9 - Batch SGD with momentum/48.9 - Batch SGD with momentum..mp4 89.2 MB20.18 - Bias-Variance tradeoff/20.18 - Bias-Variance tradeoff.mp4 88.2 MB38.1 - Problem formulation Movie reviews/38.1 - Problem formulation Movie reviews.mp4 88.1 MB57.19 - Inner, Left, Right and Outer joins/57.19 - Inner, Left, Right and Outer joins..mp4 87.6 MB47.12 - Vanishing Gradient problem/47.12 - Vanishing Gradient problem..mp4 86.3 MB55.2 - Dataset understanding/55.2 - Dataset understanding.mp4 85.7 MB28.2 - Mathematical derivation/28.2 - Mathematical derivation.mp4 85.3 MB48.2 - Dropout layers & Regularization/48.2 - Dropout layers & Regularization..mp4 85.0 MB50.16 - What is Transfer learning/50.16 - What is Transfer learning..mp4 84.5 MB50.17 - Code example Cats vs Dogs/50.17 - Code example Cats vs Dogs..mp4 84.4 MB40.10 - Data Modeling Multi label Classification/40.10 - Data Modeling Multi label Classification.mp4 83.9 MB46.14 - Data PreparationClusteringSegmentation/46.14 - Data PreparationClusteringSegmentation.mp4 83.3 MB11.18 - Applications of non-gaussian distributions/11.18 - Applications of non-gaussian distributions.mp4 82.9 MB45.8 - Exploratory Data Analysis “Random” Model/45.8 - Exploratory Data Analysis “Random” Model.mp4 82.2 MB45.10 - Univariate AnalysisVariation Feature/45.10 - Univariate AnalysisVariation Feature.mp4 81.0 MB50.15 - Inception Network/50.15 - Inception Network..mp4 80.2 MB24.1 - Geometric intuition of Logistic Regression/24.1 - Geometric intuition of Logistic Regression.mp4 79.6 MB49.1 - Tensorflow and Keras overview/49.1 - Tensorflow and Keras overview.mp4 79.4 MB23.3 - Bayes Theorem with examples/23.3 - Bayes Theorem with examples.mp4 78.8 MB40.5 - Mapping to an ML problemPerformance metrics/40.5 - Mapping to an ML problemPerformance metrics..mp4 78.6 MB50.3 - ConvolutionPadding and strides/50.3 - ConvolutionPadding and strides.mp4 77.0 MB50.12 - AlexNet/50.12 - AlexNet.mp4 77.0 MB47.10 - Backpropagation/47.10 - Backpropagation..mp4 76.6 MB50.11 - Convolution Layers in Keras/50.11 - Convolution Layers in Keras.mp4 76.5 MB2.5 - Variables and data types in Python/2.5 - Variables and data types in Python.mp4.mkv 75.3 MB24.7 - Probabilistic Interpretation Gaussian Naive Bayes/24.7 - Probabilistic Interpretation Gaussian Naive Bayes.mp4 75.0 MB42.18 - Code for Average Word2Vec product similarity/42.18 - Code for Average Word2Vec product similarity.mp4 74.8 MB17.4 - Bag of Words (BoW)/17.4 - Bag of Words (BoW).mp4 74.8 MB48.7 - OptimizersHill descent in 3D and contours/48.7 - OptimizersHill descent in 3D and contours..mp4 74.7 MB45.11 - Univariate AnalysisText feature/45.11 - Univariate AnalysisText feature.mp4 73.1 MB26.1 - Differentiation/26.1 - Differentiation.mp4 72.5 MB47.6 - Notation/47.6 - Notation.mp4 72.4 MB17.11 - Bag of Words( Code Sample)/17.11 - Bag of Words( Code Sample).mp4 72.3 MB34.12 - VC dimension/34.12 - VC dimension.mp4 71.9 MB17.2 - Data Cleaning Deduplication/17.2 - Data Cleaning Deduplication.mp4 71.7 MB47.7 - Training a single-neuron model/47.7 - Training a single-neuron model..mp4 71.6 MB9.1 - Introduction to IRIS dataset and 2D scatter plot/9.1 - Introduction to IRIS dataset and 2D scatter plot.mp4.mkv 71.4 MB44.11 - Computing Similarity matricesUser-User similarity matrix/44.11 - Computing Similarity matricesUser-User similarity matrix.mp4 71.2 MB24.15 - Non-linearly separable data & feature engineering/24.15 - Non-linearly separable data & feature engineering.mp4 70.7 MB15.5 - How to apply t-SNE and interpret its output/15.5 - How to apply t-SNE and interpret its output.mp4 70.6 MB44.23 - Surprise KNN predictors/44.23 - Surprise KNN predictors.mp4 69.4 MB45.4 - ML problem formulation Mapping real world to ML problem#/45.4 - ML problem formulation Mapping real world to ML problem..mp4 69.3 MB48.19 - Word2Vec CBOW/48.19 - Word2Vec CBOW.mp4 68.9 MB54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer.mp4 68.3 MB11.29 - Hypothesis Testing Intution with coin toss example/11.29 - Hypothesis Testing Intution with coin toss example.mp4 67.3 MB28.14 - Code Sample/28.14 - Code Sample.mp4 66.8 MB51.3 - Training RNNs Backprop/51.3 - Training RNNs Backprop..mp4 66.6 MB32.14 - XGBoost Boosting + Randomization/32.14 - XGBoost Boosting + Randomization.mp4 65.7 MB57.1 - Introduction to Databases/57.1 - Introduction to Databases.mp4 65.7 MB24.5 - L2 Regularization Overfitting and Underfitting/24.5 - L2 Regularization Overfitting and Underfitting.mp4 65.2 MB35.8 - How to initialize K-Means++/35.8 - How to initialize K-Means++.mp4 65.0 MB3.5 - Dictionary/3.5 - Dictionary.mp4 65.0 MB42.9 - Remove duplicates Part 2/42.9 - Remove duplicates Part 2.mp4 64.6 MB53.11 - Train the model/53.11 - Train the model..mp4 64.2 MB4.9 - Exception Handling/4.9 - Exception Handling.mp4 63.7 MB34.11 - Data Science Life cycle/34.11 - Data Science Life cycle.mp4.mkv 63.3 MB50.5 - Convolutional layer/50.5 - Convolutional layer..mp4 63.1 MB35.3 - Applications/35.3 - Applications.mp4 63.0 MB11.31 - K-S Test for similarity of two distributions/11.31 - K-S Test for similarity of two distributions.mp4 62.8 MB47.1 - History of Neural networks and Deep Learning/47.1 - History of Neural networks and Deep Learning..mp4 62.6 MB11.35 - How to use hypothesis testing/11.35 - How to use hypothesis testing.mp4 62.5 MB21.2 - Confusion matrix, TPR, FPR, FNR, TNR/21.2 - Confusion matrix, TPR, FPR, FNR, TNR.mp4 62.3 MB33.2 - Moving window for Time Series Data/33.2 - Moving window for Time Series Data.mp4 61.7 MB49.2 - GPU vs CPU for Deep Learning/49.2 - GPU vs CPU for Deep Learning..mp4 61.7 MB47.14 - Decision surfaces Playground/47.14 - Decision surfaces Playground.mp4 61.2 MB20.15 - Handling categorical and numerical features/20.15 - Handling categorical and numerical features.mp4 61.0 MB57.8 - SELECT/57.8 - SELECT.mp4 60.9 MB11.16 - Power law distribution/11.16 - Power law distribution.mp4 60.8 MB4.10 - Debugging Python/4.10 - Debugging Python.mp4 60.8 MB23.8 - LaplaceAdditive Smoothing/23.8 - LaplaceAdditive Smoothing.mp4 60.6 MB17.12 - Text Preprocessing( Code Sample)/17.12 - Text Preprocessing( Code Sample).mp4 60.4 MB30.6 - Building a decision Tree Constructing a DT/30.6 - Building a decision Tree Constructing a DT.mp4 60.2 MB24.3 - Mathematical formulation of Objective function/24.3 - Mathematical formulation of Objective function.mp4 59.8 MB11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/11.3 - GaussianNormal Distribution and its PDF(Probability Density Function).mp4.mkv 59.7 MB47.5 - Multi-Layered Perceptron (MLP)/47.5 - Multi-Layered Perceptron (MLP)..mp4 59.1 MB48.5 - Batch Normalization/48.5 - Batch Normalization..mp4 59.0 MB18.31 - Code SampleCross Validation/18.31 - Code SampleCross Validation.mp4 58.5 MB20.2 - Imbalanced vs balanced dataset/20.2 - Imbalanced vs balanced dataset.mp4 58.4 MB38.14 - Code example/38.14 - Code example..mp4 58.1 MB38.6 - Matrix Factorization for Collaborative filtering/38.6 - Matrix Factorization for Collaborative filtering.mp4 57.6 MB38.4 - Matrix Factorization PCA, SVD/38.4 - Matrix Factorization PCA, SVD.mp4 57.4 MB6.1 - Getting started with Matplotlib/6.1 - Getting started with Matplotlib.mp4 57.3 MB18.11 - Decision surface for K-NN as K changes/18.11 - Decision surface for K-NN as K changes.mp4 57.2 MB56.11 - PageRank/56.11 - PageRank.mp4 57.2 MB18.12 - Overfitting and Underfitting/18.12 - Overfitting and Underfitting.mp4 57.1 MB34.10 - AB testing/34.10 - AB testing..mp4 57.1 MB48.4 - Weight initialization/48.4 - Weight initialization..mp4 56.9 MB17.15 - Word2Vec (Code Sample)/17.15 - Word2Vec (Code Sample).mp4 56.6 MB33.3 - Fourier decomposition/33.3 - Fourier decomposition.mp4 56.3 MB25.4 - Code sample for Linear Regression/25.4 - Code sample for Linear Regression.mp4 56.0 MB51.1 - Why RNNs/51.1 - Why RNNs.mp4 55.6 MB17.7 - tf-idf (term frequency- inverse document frequency)/17.7 - tf-idf (term frequency- inverse document frequency).mp4 55.4 MB24.9 - hyperparameters and random search/24.9 - hyperparameters and random search.mp4 55.4 MB38.12 - Word vectors as MF/38.12 - Word vectors as MF.mp4 55.4 MB20.14 - Feature Importance and Forward Feature selection/20.14 - Feature Importance and Forward Feature selection.mp4 55.4 MB11.11 - Chebyshev’s inequality/11.11 - Chebyshev’s inequality.mp4 55.2 MB20.16 - Handling missing values by imputation/20.16 - Handling missing values by imputation.mp4 55.0 MB18.13 - Need for Cross validation/18.13 - Need for Cross validation.mp4 54.9 MB28.8 - RBF-Kernel/28.8 - RBF-Kernel.mp4 54.5 MB48.20 - Word2Vec Skip-gram/48.20 - Word2Vec Skip-gram.mp4 54.5 MB20.5 - Train and test set differences/20.5 - Train and test set differences.mp4 54.4 MB54.2 - Music representation/54.2 - Music representation.mp4 54.2 MB11.20 - Pearson Correlation Coefficient/11.20 - Pearson Correlation Coefficient.mp4 54.2 MB49.7 - MLP Initialization/49.7 - MLP Initialization.mp4 53.5 MB54.7 - Char-RNN with abc-notation Model architecture,Model training/54.7 - Char-RNN with abc-notation Model architecture,Model training..mp4 53.1 MB38.13 - Eigen-Faces/38.13 - Eigen-Faces.mp4 52.9 MB38.8 - Clustering as MF/38.8 - Clustering as MF.mp4 52.1 MB4.1 - Introduction/4.1 - Introduction.mp4 52.0 MB56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/56.10 - Feature engineering on GraphsJaccard & Cosine Similarities.mp4 51.9 MB21.6 - R-SquaredCoefficient of determination/21.6 - R-SquaredCoefficient of determination.mp4 51.9 MB51.4 - Types of RNNs/51.4 - Types of RNNs..mp4 51.8 MB42.8 - Remove duplicates Part 1/42.8 - Remove duplicates Part 1.mp4 51.7 MB26.2 - Online differentiation tools/26.2 - Online differentiation tools.mp4 51.5 MB11.15 - Log Normal Distribution/11.15 - Log Normal Distribution.mp4 51.3 MB42.15 - Code for TF-IDF based product similarity/42.15 - Code for TF-IDF based product similarity.mp4 50.6 MB21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC.mp4 50.6 MB3.4 - Sets/3.4 - Sets.mp4 50.6 MB42.10 - Text Pre-Processing Tokenization and Stop-word removal/42.10 - Text Pre-Processing Tokenization and Stop-word removal.mp4 50.5 MB40.1 - BusinessReal world problem/40.1 - BusinessReal world problem.mp4 50.4 MB4.4 - Recursive functions/4.4 - Recursive functions.mp4 50.3 MB8.1 - Space and Time Complexity Find largest number in a list/8.1 - Space and Time Complexity Find largest number in a list.mp4 50.3 MB56.8 - EDABinary Classification Task/56.8 - EDABinary Classification Task.mp4 50.1 MB43.3 - Machine Learning problem mapping Data overview/43.3 - Machine Learning problem mapping Data overview..mp4 49.8 MB48.8 - SGD Recap/48.8 - SGD Recap.mp4 49.6 MB50.13 - VGGNet/50.13 - VGGNet.mp4 49.4 MB34.7 - Modeling in the presence of outliers RANSAC/34.7 - Modeling in the presence of outliers RANSAC.mp4 49.4 MB28.1 - Geometric Intution/28.1 - Geometric Intution.mp4 49.4 MB11.14 - Bernoulli and Binomial Distribution/11.14 - Bernoulli and Binomial Distribution.mp4 49.3 MB46.1 - BusinessReal world problem Overview/46.1 - BusinessReal world problem Overview.mp4 49.2 MB2.9 - Control flow while loop/2.9 - Control flow while loop.mp4 49.1 MB48.21 - Word2Vec Algorithmic Optimizations/48.21 - Word2Vec Algorithmic Optimizations..mp4 49.0 MB18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming.mp4 49.0 MB24.8 - Loss minimization interpretation/24.8 - Loss minimization interpretation.mp4 48.8 MB11.34 - Resampling and Permutation test another example/11.34 - Resampling and Permutation test another example.mp4 48.7 MB40.8 - EDAAnalysis of tags/40.8 - EDAAnalysis of tags.mp4 48.6 MB34.8 - Productionizing models/34.8 - Productionizing models.mp4 48.4 MB45.15 - Logistic Regression with class balancing/45.15 - Logistic Regression with class balancing.mp4 48.4 MB50.1 - Biological inspiration Visual Cortex/50.1 - Biological inspiration Visual Cortex.mp4 48.4 MB3.6 - Strings/3.6 - Strings.mp4 48.3 MB18.16 - How to determine overfitting and underfitting/18.16 - How to determine overfitting and underfitting.mp4 48.3 MB47.3 - Growth of biological neural networks/47.3 - Growth of biological neural networks.mp4 48.2 MB48.6 - OptimizersHill-descent analogy in 2D/48.6 - OptimizersHill-descent analogy in 2D.mp4 48.2 MB42.5 - Overview of the data and Terminology/42.5 - Overview of the data and Terminology.mp4 48.1 MB56.13 - Connected-components/56.13 - Connected-components.mp4 47.8 MB33.18 - Kaggle Winners solutions/33.18 - Kaggle Winners solutions.mp4 47.8 MB35.10 - K-Medoids/35.10 - K-Medoids.mp4 47.5 MB40.9 - EDAData Preprocessing/40.9 - EDAData Preprocessing.mp4 47.3 MB11.27 - Confidence interval using bootstrapping/11.27 - Confidence interval using bootstrapping.mp4 47.2 MB55.7 - Deep-learning Model/55.7 - Deep-learning Model..mp4 47.0 MB57.12 - WHERE, Comparison operators, NULL/57.12 - WHERE, Comparison operators, NULL.mp4 47.0 MB57.16 - HAVING/57.16 - HAVING.mp4 47.0 MB43.14 - ASM Files Feature extraction & Multiprocessing/43.14 - ASM Files Feature extraction & Multiprocessing..mp4 46.9 MB55.6 - Classical ML models/55.6 - Classical ML models..mp4 46.8 MB18.17 - Time based splitting/18.17 - Time based splitting.mp4 46.6 MB18.7 - Cosine Distance & Cosine Similarity/18.7 - Cosine Distance & Cosine Similarity.mp4 46.6 MB11.36 - Proportional Sampling/11.36 - Proportional Sampling.mp4 46.6 MB26.5 - Gradient descent geometric intuition/26.5 - Gradient descent geometric intuition.mp4 46.6 MB18.22 - How to build a kd-tree/18.22 - How to build a kd-tree.mp4 46.6 MB30.3 - Building a decision TreeEntropy/30.3 - Building a decision TreeEntropy.mp4 46.5 MB28.4 - Loss function (Hinge Loss) based interpretation/28.4 - Loss function (Hinge Loss) based interpretation.mp4 46.4 MB18.14 - K-fold cross validation/18.14 - K-fold cross validation.mp4 46.4 MB45.1 - BusinessReal world problem Overview/45.1 - BusinessReal world problem Overview.mp4 46.3 MB57.23 - DDLCREATE TABLE/57.23 - DDLCREATE TABLE.mp4 46.3 MB11.33 - Hypothesis testing another example/11.33 - Hypothesis testing another example.mp4 46.2 MB18.23 - Find nearest neighbours using kd-tree/18.23 - Find nearest neighbours using kd-tree.mp4 46.2 MB35.9 - Failure casesLimitations/35.9 - Failure casesLimitations.mp4 46.1 MB41.15 - ML Models Logistic Regression and Linear SVM/41.15 - ML Models Logistic Regression and Linear SVM.mp4 45.8 MB47.11 - Activation functions/47.11 - Activation functions.mp4 45.6 MB26.11 - Why L1 regularization creates sparsity/26.11 - Why L1 regularization creates sparsity.mp4 45.4 MB37.7 - Advantages and Limitations of DBSCAN/37.7 - Advantages and Limitations of DBSCAN.mp4 44.7 MB30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes.mp4 44.6 MB8.2 - Binary search/8.2 - Binary search.mp4 44.6 MB48.1 - Deep Multi-layer perceptrons1980s to 2010s/48.1 - Deep Multi-layer perceptrons1980s to 2010s.mp4 44.5 MB57.15 - GROUP BY/57.15 - GROUP BY.mp4 44.3 MB32.2 - Bootstrapped Aggregation (Bagging) Intuition/32.2 - Bootstrapped Aggregation (Bagging) Intuition.mp4 44.0 MB45.14 - K-Nearest Neighbors Classification/45.14 - K-Nearest Neighbors Classification.mp4 43.7 MB54.1 - Real-world problem/54.1 - Real-world problem.mp4 43.6 MB50.4 - Convolution over RGB images/50.4 - Convolution over RGB images..mp4 43.6 MB42.14 - TF-IDF featurizing text based on word-importance/42.14 - TF-IDF featurizing text based on word-importance.mp4 43.0 MB47.4 - Diagrammatic representation Logistic Regression and Perceptron/47.4 - Diagrammatic representation Logistic Regression and Perceptron.mp4 42.8 MB30.14 - Code Samples/30.14 - Code Samples.mp4 42.8 MB45.12 - Machine Learning ModelsData preparation/45.12 - Machine Learning ModelsData preparation.mp4 42.5 MB41.10 - EDA Feature analysis/41.10 - EDA Feature analysis..mp4 42.3 MB11.10 - How distributions are used/11.10 - How distributions are used.mp4 42.2 MB18.21 - Binary search tree/18.21 - Binary search tree.mp4 42.1 MB28.5 - Dual form of SVM formulation/28.5 - Dual form of SVM formulation.mp4 42.1 MB54.6 - Char-RNN with abc-notation State full RNN/54.6 - Char-RNN with abc-notation State full RNN.mp4 42.1 MB17.9 - Word2Vec/17.9 - Word2Vec..mp4 41.8 MB2.1 - Python, Anaconda and relevant packages installations/2.1 - Python, Anaconda and relevant packages installations.mp4.mkv 41.7 MB32.9 - Boosting Intuition/32.9 - Boosting Intuition.mp4 41.6 MB42.16 - Code for IDF based product similarity/42.16 - Code for IDF based product similarity.mp4 41.5 MB41.7 - EDA Basic Feature Extraction/41.7 - EDA Basic Feature Extraction.mp4 41.4 MB38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/38.10 - Matrix Factorization for recommender systems Netflix Prize Solution.mp4 41.4 MB43.10 - ML models – using byte files only Random Model/43.10 - ML models – using byte files only Random Model.mp4 41.3 MB57.18 - Join and Natural Join/57.18 - Join and Natural Join.mp4 41.3 MB56.19 - Modeling/56.19 - Modeling.mp4 41.1 MB34.3 - Calibration Plots/34.3 - Calibration Plots..mp4 41.0 MB56.6 - EDABasic Stats/56.6 - EDABasic Stats.mp4 40.8 MB18.8 - How to measure the effectiveness of k-NN/18.8 - How to measure the effectiveness of k-NN.mp4 40.7 MB57.7 - USE, DESCRIBE, SHOW TABLES/57.7 - USE, DESCRIBE, SHOW TABLES.mp4 40.7 MB49.10 - Model 3 Batch Normalization/49.10 - Model 3 Batch Normalization..mp4 40.6 MB23.20 - Code example/23.20 - Code example.mp4 40.5 MB18.15 - Visualizing train, validation and test datasets/18.15 - Visualizing train, validation and test datasets.mp4 40.5 MB46.2 - Objectives and Constraints/46.2 - Objectives and Constraints.mp4 40.4 MB43.18 - ML models on ASM file features/43.18 - ML models on ASM file features.mp4 40.3 MB33.6 - Keypoints SIFT/33.6 - Keypoints SIFT..mp4 40.2 MB45.20 - Stacking Classifier/45.20 - Stacking Classifier.mp4 40.1 MB38.3 - Similarity based Algorithms/38.3 - Similarity based Algorithms.mp4 40.0 MB53.2 - Datasets#/53.2 - Datasets..mp4 40.0 MB44.18 - Featurizations for regression/44.18 - Featurizations for regression..mp4 39.9 MB4.3 - Function arguments/4.3 - Function arguments.mp4 39.9 MB57.4 - IMDB dataset/57.4 - IMDB dataset.mp4 39.7 MB47.9 - Training an MLPMemoization/47.9 - Training an MLPMemoization.mp4 39.2 MB57.5 - Installing MySQL/57.5 - Installing MySQL.mp4 39.0 MB23.7 - Naive Bayes on Text data/23.7 - Naive Bayes on Text data.mp4 38.8 MB50.8 - Example CNN LeNet [1998]/50.8 - Example CNN LeNet [1998].mp4 38.6 MB20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/20.7 - Local outlier Factor (Simple solution Mean distance to Knn).mp4 38.6 MB33.5 - Image histogram/33.5 - Image histogram.mp4 38.6 MB56.9 - EDATrain and test split/56.9 - EDATrain and test split..mp4 38.5 MB44.7 - Exploratory Data AnalysisPreliminary data analysis/44.7 - Exploratory Data AnalysisPreliminary data analysis..mp4 38.4 MB13.7 - Data Preprocessing Column Standardization/13.7 - Data Preprocessing Column Standardization.mp4 38.3 MB53.1 - Self Driving Car Problem definition/53.1 - Self Driving Car Problem definition..mp4 38.1 MB46.3 - Mapping to ML problem Data/46.3 - Mapping to ML problem Data.mp4 37.7 MB54.8 - Char-RNN with abc-notation Music generation/54.8 - Char-RNN with abc-notation Music generation..mp4 37.3 MB11.26 - C.I for mean of a normal random variable/11.26 - C.I for mean of a normal random variable.mp4 37.3 MB57.27 - Learning resources/57.27 - Learning resources.mp4 37.2 MB56.16 - HITS Score/56.16 - HITS Score.mp4 37.1 MB36.1 - Agglomerative & Divisive, Dendrograms/36.1 - Agglomerative & Divisive, Dendrograms.mp4 37.0 MB33.1 - Introduction/33.1 - Introduction.mp4 37.0 MB47.13 - Bias-Variance tradeoff/47.13 - Bias-Variance tradeoff..mp4 36.9 MB46.4 - Mapping to ML problem dask dataframes/46.4 - Mapping to ML problem dask dataframes.mp4 36.8 MB23.15 - Handling Numerical features (Gaussian NB)/23.15 - Handling Numerical features (Gaussian NB).mp4 36.7 MB32.3 - Random Forest and their construction/32.3 - Random Forest and their construction.mp4 36.7 MB13.9 - MNIST dataset (784 dimensional)/13.9 - MNIST dataset (784 dimensional).mp4 36.5 MB21.1 - Accuracy/21.1 - Accuracy.mp4 36.5 MB45.18 - Random-Forest with one-hot encoded features/45.18 - Random-Forest with one-hot encoded features.mp4 36.3 MB32.17 - Cascading classifiers/32.17 - Cascading classifiers.mp4 36.3 MB48.11 - OptimizersAdaGrad/48.11 - OptimizersAdaGrad.mp4 36.2 MB23.12 - Imbalanced data/23.12 - Imbalanced data.mp4 36.2 MB42.22 - Code for weighted similarity/42.22 - Code for weighted similarity.mp4 36.0 MB56.14 - Adar Index/56.14 - Adar Index.mp4 35.8 MB56.7 - EDAFollower and following stats/56.7 - EDAFollower and following stats..mp4 35.7 MB57.2 - Why SQL/57.2 - Why SQL.mp4 35.7 MB26.9 - Constrained Optimization & PCA/26.9 - Constrained Optimization & PCA.mp4 35.7 MB17.6 - uni-gram, bi-gram, n-grams/17.6 - uni-gram, bi-gram, n-grams..mp4 35.6 MB13.10 - Code to Load MNIST Data Set/13.10 - Code to Load MNIST Data Set.mp4 35.6 MB23.1 - Conditional probability/23.1 - Conditional probability.mp4 35.6 MB46.24 - Regression models Train-Test split & Features/46.24 - Regression models Train-Test split & Features.mp4 35.6 MB33.11 - Feature binning/33.11 - Feature binning.mp4 35.5 MB23.10 - Bias and Variance tradeoff/23.10 - Bias and Variance tradeoff.mp4 35.4 MB24.11 - Feature importance and Model interpretability/24.11 - Feature importance and Model interpretability.mp4 35.2 MB24.12 - Collinearity of features/24.12 - Collinearity of features.mp4 35.2 MB25.2 - Mathematical formulation/25.2 - Mathematical formulation.mp4 35.1 MB45.6 - Exploratory Data AnalysisReading data & preprocessing/45.6 - Exploratory Data AnalysisReading data & preprocessing.mp4 35.1 MB36.2 - Agglomerative Clustering/36.2 - Agglomerative Clustering.mp4 35.0 MB13.8 - Co-variance of a Data Matrix/13.8 - Co-variance of a Data Matrix.mp4 34.9 MB50.9 - ImageNet dataset/50.9 - ImageNet dataset..mp4 34.9 MB42.25 - Using Keras + Tensorflow to extract features/42.25 - Using Keras + Tensorflow to extract features.mp4 34.7 MB17.8 - Why use log in IDF/17.8 - Why use log in IDF.mp4 34.6 MB17.3 - Why convert text to a vector/17.3 - Why convert text to a vector.mp4 34.1 MB14.10 - PCA for dimensionality reduction (not-visualization)/14.10 - PCA for dimensionality reduction (not-visualization).mp4 33.9 MB40.14 - Logistic regression One VS Rest/40.14 - Logistic regression One VS Rest.mp4 33.8 MB21.5 - Log-loss/21.5 - Log-loss.mp4 33.7 MB14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction.mp4 33.7 MB46.20 - Simple moving average/46.20 - Simple moving average.mp4 33.7 MB7.1 - Getting started with pandas/7.1 - Getting started with pandas.mp4 33.1 MB25.1 - Geometric intuition of Linear Regression/25.1 - Geometric intuition of Linear Regression.mp4 33.1 MB56.17 - SVD/56.17 - SVD.mp4 33.0 MB11.30 - Resampling and permutation test/11.30 - Resampling and permutation test.mp4 33.0 MB11.17 - Box cox transform/11.17 - Box cox transform.mp4 32.7 MB49.13 - Hyperparameter tuning in Keras/49.13 - Hyperparameter tuning in Keras..mp4 32.6 MB43.7 - Exploratory Data Analysis Feature extraction from byte files/43.7 - Exploratory Data Analysis Feature extraction from byte files.mp4 32.4 MB44.15 - Overview of the modelling strategy/44.15 - Overview of the modelling strategy..mp4 32.3 MB20.12 - Impact of Scale & Column standardization/20.12 - Impact of Scale & Column standardization.mp4 32.3 MB35.12 - Code Samples/35.12 - Code Samples.mp4 32.3 MB45.17 - Linear-SVM/45.17 - Linear-SVM..mp4 32.0 MB51.5 - Need for LSTMGRU/51.5 - Need for LSTMGRU..mp4 31.9 MB32.10 - Residuals, Loss functions and gradients/32.10 - Residuals, Loss functions and gradients.mp4 31.8 MB18.28 - LSH for euclidean distance/18.28 - LSH for euclidean distance.mp4 31.8 MB35.4 - Metrics for Clustering/35.4 - Metrics for Clustering.mp4 31.7 MB8.4 - Find elements common in two lists using a HashtableDict/8.4 - Find elements common in two lists using a HashtableDict.mp4 31.5 MB43.13 - Random Forest and Xgboost/43.13 - Random Forest and Xgboost.mp4 31.5 MB37.5 - DBSCAN Algorithm/37.5 - DBSCAN Algorithm.mp4 31.5 MB33.9 - Graph data/33.9 - Graph data.mp4 31.4 MB45.7 - Exploratory Data AnalysisDistribution of Class-labels/45.7 - Exploratory Data AnalysisDistribution of Class-labels.mp4 31.3 MB44.8 - Exploratory Data AnalysisSparse matrix representation/44.8 - Exploratory Data AnalysisSparse matrix representation.mp4 31.2 MB30.13 - Cases/30.13 - Cases.mp4 31.1 MB46.18 - Data Preparation Time series and Fourier transforms/46.18 - Data Preparation Time series and Fourier transforms..mp4 31.1 MB2.10 - Control flow for loop/2.10 - Control flow for loop.mp4.mkv 30.9 MB11.23 - How to use correlations/11.23 - How to use correlations.mp4 30.9 MB18.9 - TestEvaluation time and space complexity/18.9 - TestEvaluation time and space complexity.mp4 30.8 MB49.4 - Install TensorFlow/49.4 - Install TensorFlow.mp4 30.6 MB45.2 - Business objectives and constraints/45.2 - Business objectives and constraints..mp4 30.5 MB20.13 - Interpretability/20.13 - Interpretability.mp4 30.4 MB32.11 - Gradient Boosting/32.11 - Gradient Boosting.mp4 30.1 MB11.19 - Co-variance/11.19 - Co-variance.mp4 30.0 MB43.4 - Machine Learning problem mapping ML problem/43.4 - Machine Learning problem mapping ML problem.mp4 30.0 MB49.9 - Model 2 ReLU activation/49.9 - Model 2 ReLU activation..mp4 29.9 MB26.3 - Maxima and Minima/26.3 - Maxima and Minima.mp4 29.8 MB20.3 - Multi-class classification/20.3 - Multi-class classification.mp4 29.6 MB57.11 - DISTINCT/57.11 - DISTINCT.mp4 29.5 MB51.9 - Bidirectional RNN/51.9 - Bidirectional RNN..mp4 29.1 MB15.7 - Code example of t-SNE/15.7 - Code example of t-SNE.mp4 29.1 MB56.3 - Data format & Limitations/56.3 - Data format & Limitations..mp4 29.0 MB44.1 - BusinessReal world problemProblem definition/44.1 - BusinessReal world problemProblem definition.mp4 28.9 MB28.7 - Polynomial Kernel/28.7 - Polynomial Kernel.mp4 28.8 MB44.5 - Exploratory Data AnalysisData preprocessing/44.5 - Exploratory Data AnalysisData preprocessing.mp4 28.6 MB9.8 - Mean, Variance and Standard Deviation/9.8 - Mean, Variance and Standard Deviation.mp4 28.6 MB14.3 - Mathematical objective function of PCA/14.3 - Mathematical objective function of PCA.mp4 28.6 MB18.4 - K-Nearest Neighbours Geometric intuition with a toy example/18.4 - K-Nearest Neighbours Geometric intuition with a toy example.mp4 28.6 MB33.15 - Feature orthogonality/33.15 - Feature orthogonality.mp4 28.5 MB48.13 - Adam/48.13 - Adam.mp4 28.4 MB50.6 - Max-pooling/50.6 - Max-pooling..mp4 28.3 MB23.9 - Log-probabilities for numerical stability/23.9 - Log-probabilities for numerical stability.mp4 28.3 MB38.2 - Content based vs Collaborative Filtering/38.2 - Content based vs Collaborative Filtering.mp4 28.2 MB44.14 - ML ModelsSurprise library/44.14 - ML ModelsSurprise library.mp4 28.2 MB44.25 - SVD ++ with implicit feedback/44.25 - SVD ++ with implicit feedback.mp4 28.1 MB49.5 - Online documentation and tutorials/49.5 - Online documentation and tutorials.mp4 28.1 MB53.4 - Dash-cam images and steering angles/53.4 - Dash-cam images and steering angles..mp4 28.0 MB33.8 - Relational data/33.8 - Relational data.mp4 28.0 MB34.4 - Platt’s CalibrationScaling/34.4 - Platt’s CalibrationScaling..mp4 28.0 MB57.9 - LIMIT, OFFSET/57.9 - LIMIT, OFFSET.mp4 27.9 MB9.9 - Median/9.9 - Median.mp4 27.9 MB42.26 - Visual similarity based product similarity/42.26 - Visual similarity based product similarity.mp4 27.6 MB45.19 - Random-Forest with response-coded features/45.19 - Random-Forest with response-coded features.mp4 27.5 MB3.2 - Tuples part 1/3.2 - Tuples part 1.mp4 27.4 MB11.13 - How to randomly sample data points (Uniform Distribution)/11.13 - How to randomly sample data points (Uniform Distribution).mp4 27.4 MB21.3 - Precision and recall, F1-score/21.3 - Precision and recall, F1-score.mp4 27.3 MB51.7 - GRUs/51.7 - GRUs..mp4 27.3 MB44.21 - Surprise Baseline model/44.21 - Surprise Baseline model..mp4 27.2 MB11.25 - Computing confidence interval given the underlying distribution/11.25 - Computing confidence interval given the underlying distribution.mp4 27.2 MB35.6 - K-Means Mathematical formulation Objective function/35.6 - K-Means Mathematical formulation Objective function.mp4 27.2 MB24.6 - L1 regularization and sparsity/24.6 - L1 regularization and sparsity.mp4 27.1 MB24.14 - Real world cases/24.14 - Real world cases.mp4 27.0 MB7.2 - Data Frame Basics/7.2 - Data Frame Basics.mp4 27.0 MB46.7 - Mapping to ML problem Performance metrics/46.7 - Mapping to ML problem Performance metrics.mp4 26.9 MB42.2 - Plan of action/42.2 - Plan of action.mp4 26.9 MB11.8 - Sampling distribution & Central Limit theorem/11.8 - Sampling distribution & Central Limit theorem.mp4 26.8 MB44.13 - Computing Similarity matricesDoes movie-movie similarity work/44.13 - Computing Similarity matricesDoes movie-movie similarity work.mp4 26.8 MB24.18 - Extensions to Generalized linear models/24.18 - Extensions to Generalized linear models.mp4 26.7 MB35.7 - K-Means Algorithm/35.7 - K-Means Algorithm..mp4 26.7 MB53.9 - Batch load the dataset/53.9 - Batch load the dataset..mp4 26.6 MB24.13 - TestRun time space and time complexity/24.13 - TestRun time space and time complexity.mp4 26.5 MB50.18 - Code Example MNIST dataset/50.18 - Code Example MNIST dataset..mp4 26.4 MB41.1 - BusinessReal world problem Problem definition/41.1 - BusinessReal world problem Problem definition.mp4 26.4 MB41.13 - ML Models Loading Data/41.13 - ML Models Loading Data.mp4 26.3 MB24.4 - Weight vector/24.4 - Weight vector.mp4 26.3 MB30.4 - Building a decision TreeInformation Gain/30.4 - Building a decision TreeInformation Gain.mp4 26.2 MB26.8 - SGD algorithm/26.8 - SGD algorithm.mp4 26.1 MB34.5 - Isotonic Regression/34.5 - Isotonic Regression.mp4 25.9 MB42.7 - Understand duplicate rows/42.7 - Understand duplicate rows.mp4 25.9 MB9.7 - CDF(Cumulative Distribution Function)/9.7 - CDF(Cumulative Distribution Function).mp4 25.8 MB18.1 - How “Classification” works/18.1 - How “Classification” works.mp4 25.7 MB46.22 - Exponential weighted moving average/46.22 - Exponential weighted moving average.mp4 25.7 MB13.5 - Data Preprocessing Feature Normalisation/13.5 - Data Preprocessing Feature Normalisation.mkv 25.7 MB23.11 - Feature importance and interpretability/23.11 - Feature importance and interpretability.mp4 25.7 MB41.12 - EDA TF-IDF weighted Word2Vec featurization/41.12 - EDA TF-IDF weighted Word2Vec featurization..mp4 25.6 MB48.15 - Gradient Checking and clipping/48.15 - Gradient Checking and clipping.mp4 25.6 MB46.15 - Data PreparationTime binning/46.15 - Data PreparationTime binning.mp4 25.5 MB57.21 - DMLINSERT/57.21 - DMLINSERT.mp4 25.4 MB49.11 - Model 4 Dropout/49.11 - Model 4 Dropout..mp4 25.4 MB35.1 - What is Clustering/35.1 - What is Clustering.mp4 25.2 MB18.26 - Hashing vs LSH/18.26 - Hashing vs LSH.mp4 25.1 MB38.9 - Hyperparameter tuning/38.9 - Hyperparameter tuning.mp4 25.1 MB26.4 - Vector calculus Grad/26.4 - Vector calculus Grad.mp4 25.0 MB11.21 - Spearman Rank Correlation Coefficient/11.21 - Spearman Rank Correlation Coefficient.mp4 24.9 MB28.6 - kernel trick/28.6 - kernel trick.mp4 24.9 MB45.3 - ML problem formulation Data/45.3 - ML problem formulation Data.mp4 24.9 MB41.8 - EDA Text Preprocessing/41.8 - EDA Text Preprocessing.mp4 24.9 MB11.7 - Kernel density estimation/11.7 - Kernel density estimation.mp4 24.7 MB44.2 - Objectives and constraints/44.2 - Objectives and constraints.mp4 24.7 MB54.11 - Survey blog/54.11 - Survey blog.mp4 24.6 MB56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path..mp4 24.6 MB53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN.mp4 24.6 MB17.13 - Bi-Grams and n-grams (Code Sample)/17.13 - Bi-Grams and n-grams (Code Sample).mp4 24.5 MB32.18 - Kaggle competitions vs Real world/32.18 - Kaggle competitions vs Real world.mp4 24.4 MB11.12 - Discrete and Continuous Uniform distributions/11.12 - Discrete and Continuous Uniform distributions.mp4 24.3 MB9.3 - Pair plots/9.3 - Pair plots.mp4 24.2 MB44.20 - Xgboost with 13 features/44.20 - Xgboost with 13 features.mp4 24.1 MB46.9 - Data Cleaning Trip Duration/46.9 - Data Cleaning Trip Duration..mp4 24.1 MB41.16 - ML Models XGBoost/41.16 - ML Models XGBoost.mp4 24.0 MB46.5 - Mapping to ML problem FieldsFeatures/46.5 - Mapping to ML problem FieldsFeatures..mp4 24.0 MB14.4 - Alternative formulation of PCA Distance minimization/14.4 - Alternative formulation of PCA Distance minimization.mp4 24.0 MB14.6 - PCA for Dimensionality Reduction and Visualization/14.6 - PCA for Dimensionality Reduction and Visualization.mp4 23.9 MB18.10 - KNN Limitations/18.10 - KNN Limitations.mp4 23.8 MB37.6 - Hyper Parameters MinPts and Eps/37.6 - Hyper Parameters MinPts and Eps.mp4 23.7 MB54.10 - MIDI music generation/54.10 - MIDI music generation..mp4 23.6 MB43.1 - Businessreal world problem Problem definition/43.1 - Businessreal world problem Problem definition.mp4 23.6 MB33.17 - Feature slicing/33.17 - Feature slicing.mp4 23.5 MB42.20 - Code for IDF weighted Word2Vec product similarity/42.20 - Code for IDF weighted Word2Vec product similarity.mp4 23.5 MB20.9 - Reachability-Distance(A,B)/20.9 - Reachability-Distance(A,B).mp4 23.5 MB46.19 - Ratios and previous-time-bin values/46.19 - Ratios and previous-time-bin values.mp4 23.4 MB57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM.mp4 23.3 MB50.7 - CNN Training Optimization/50.7 - CNN Training Optimization.mp4 23.3 MB9.5 - Histogram and Introduction to PDF(Probability Density Function)/9.5 - Histogram and Introduction to PDF(Probability Density Function).mkv 23.3 MB32.12 - Regularization by Shrinkage/32.12 - Regularization by Shrinkage.mp4 23.1 MB9.15 - Multivariate Probability Density, Contour Plot/9.15 - Multivariate Probability Density, Contour Plot.mp4 23.1 MB30.12 - Regression using Decision Trees/30.12 - Regression using Decision Trees.mp4 23.0 MB42.21 - Weighted similarity using brand and color/42.21 - Weighted similarity using brand and color.mp4 22.9 MB11.1 - Introduction to Probability and Statistics/11.1 - Introduction to Probability and Statistics.mp4 22.9 MB32.7 - Extremely randomized trees/32.7 - Extremely randomized trees.mp4 22.9 MB49.3 - Google Colaboratory/49.3 - Google Colaboratory..mp4 22.7 MB55.1 - Human Activity Recognition Problem definition/55.1 - Human Activity Recognition Problem definition.mp4 22.7 MB11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution.mp4 22.6 MB17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec.mp4 22.5 MB15.6 - t-SNE on MNIST/15.6 - t-SNE on MNIST.mp4 22.5 MB11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/11.28 - Hypothesis testing methodology, Null-hypothesis, p-value.mp4 22.5 MB40.15 - Sampling data and tags+Weighted models/40.15 - Sampling data and tags+Weighted models..mp4 22.5 MB56.4 - Mapping to a supervised classification problem/56.4 - Mapping to a supervised classification problem..mp4 22.5 MB43.11 - k-NN/43.11 - k-NN.mp4 22.4 MB46.8 - Data Cleaning Latitude and Longitude data/46.8 - Data Cleaning Latitude and Longitude data.mp4 22.3 MB30.2 - Sample Decision tree/30.2 - Sample Decision tree.mp4 22.3 MB48.14 - Which algorithm to choose when/48.14 - Which algorithm to choose when.mp4 22.3 MB4.5 - Lambda functions/4.5 - Lambda functions.mp4 22.2 MB20.10 - Local reachability-density(A)/20.10 - Local reachability-density(A).mp4 22.2 MB35.5 - K-Means Geometric intuition, Centroids/35.5 - K-Means Geometric intuition, Centroids.mp4 22.1 MB28.13 - Cases/28.13 - Cases.mp4 22.1 MB43.12 - Logistic regression/43.12 - Logistic regression.mp4 22.1 MB9.10 - Percentiles and Quantiles/9.10 - Percentiles and Quantiles.mp4 22.0 MB50.10 - Data Augmentation/50.10 - Data Augmentation..mp4 22.0 MB33.14 - Model specific featurizations/33.14 - Model specific featurizations.mp4 22.0 MB45.22 - Assignments/45.22 - Assignments..mp4 21.9 MB40.13 - Featurization/40.13 - Featurization.mp4 21.9 MB45.21 - Majority Voting classifier/45.21 - Majority Voting classifier.mp4 21.7 MB9.12 - Box-plot with Whiskers/9.12 - Box-plot with Whiskers.mp4 21.7 MB48.12 - Optimizers Adadelta andRMSProp/48.12 - Optimizers Adadelta andRMSProp.mp4 21.7 MB2.7 - Operators/2.7 - Operators.mp4 21.6 MB41.6 - EDA Basic Statistics/41.6 - EDA Basic Statistics..mp4 21.5 MB46.29 - Assignment/46.29 - Assignment..mp4 21.5 MB30.9 - Building a decision TreeCategorical features with many possible values/30.9 - Building a decision TreeCategorical features with many possible values.mp4 21.4 MB32.5 - Train and run time complexity/32.5 - Train and run time complexity.mp4 21.4 MB34.6 - Code Samples/34.6 - Code Samples.mp4 21.4 MB14.2 - Geometric intuition of PCA/14.2 - Geometric intuition of PCA.mp4 21.3 MB10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces.mp4 21.2 MB55.5 - EDAData visualization using t-SNE/55.5 - EDAData visualization using t-SNE.mp4 21.2 MB38.7 - Matrix Factorization for feature engineering/38.7 - Matrix Factorization for feature engineering.mp4 21.1 MB20.4 - k-NN, given a distance or similarity matrix/20.4 - k-NN, given a distance or similarity matrix.mp4 21.1 MB15.3 - Geometric intuition of t-SNE/15.3 - Geometric intuition of t-SNE.mp4 21.0 MB28.3 - Why we take values +1 and and -1 for Support vector planes/28.3 - Why we take values +1 and and -1 for Support vector planes.mp4 20.9 MB33.12 - Interaction variables/33.12 - Interaction variables.mp4 20.9 MB34.9 - Retraining models periodically/34.9 - Retraining models periodically..mp4 20.8 MB18.24 - Limitations of Kd tree/18.24 - Limitations of Kd tree.mp4 20.7 MB42.19 - TF-IDF weighted Word2Vec/42.19 - TF-IDF weighted Word2Vec.mp4 20.7 MB57.10 - ORDER BY/57.10 - ORDER BY.mp4 20.7 MB56.5 - Business constraints & Metrics/56.5 - Business constraints & Metrics..mp4 20.6 MB46.28 - Model comparison/46.28 - Model comparison.mp4 20.6 MB47.2 - How Biological Neurons work/47.2 - How Biological Neurons work.mp4 20.6 MB48.10 - Nesterov Accelerated Gradient (NAG)/48.10 - Nesterov Accelerated Gradient (NAG).mp4 20.5 MB25.3 - Real world Cases/25.3 - Real world Cases.mp4 20.3 MB56.15 - Kartz Centrality/56.15 - Kartz Centrality.mp4 20.2 MB15.4 - Crowding Problem/15.4 - Crowding Problem.mp4 20.1 MB57.22 - DMLUPDATE , DELETE/57.22 - DMLUPDATE , DELETE.mp4 20.1 MB9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis.mp4 20.0 MB2.6 - Standard Input and Output/2.6 - Standard Input and Output.mp4 20.0 MB55.4 - EDAUnivariate analysis/55.4 - EDAUnivariate analysis..mp4 19.9 MB32.6 - BaggingCode Sample/32.6 - BaggingCode Sample.mp4 19.9 MB46.10 - Data Cleaning Speed/46.10 - Data Cleaning Speed..mp4 19.8 MB40.11 - Data preparation/40.11 - Data preparation..mp4 19.7 MB18.5 - Failure cases of KNN/18.5 - Failure cases of KNN.mp4 19.6 MB48.17 - How to train a Deep MLP/48.17 - How to train a Deep MLP.mp4 19.6 MB40.4 - Mapping to an ML problemML problem formulation/40.4 - Mapping to an ML problemML problem formulation..mp4 19.6 MB18.29 - Probabilistic class label/18.29 - Probabilistic class label.mp4 19.6 MB28.12 - SVM Regression/28.12 - SVM Regression.mp4 19.5 MB44.28 - Assignments/44.28 - Assignments..mp4 19.5 MB18.25 - Extensions/18.25 - Extensions.mp4 19.5 MB11.24 - Confidence interval (C.I) Introduction/11.24 - Confidence interval (C.I) Introduction.mp4 19.5 MB28.10 - Train and run time complexities/28.10 - Train and run time complexities.mp4 19.4 MB10.3 - Dot Product and Angle between 2 Vectors/10.3 - Dot Product and Angle between 2 Vectors.mp4 19.4 MB2.3 - Keywords and identifiers/2.3 - Keywords and identifiers.mp4 19.3 MB33.4 - Deep learning features LSTM/33.4 - Deep learning features LSTM.mp4 19.3 MB26.7 - Gradient descent for linear regression/26.7 - Gradient descent for linear regression.mp4 19.2 MB46.6 - Mapping to ML problem Time series forecastingRegression/46.6 - Mapping to ML problem Time series forecastingRegression.mp4 19.0 MB30.7 - Building a decision Tree Splitting numerical features/30.7 - Building a decision Tree Splitting numerical features.mp4 18.9 MB23.19 - Best and worst cases/23.19 - Best and worst cases.mp4 18.9 MB46.26 - Random Forest regression/46.26 - Random Forest regression.mp4 18.9 MB10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector.mp4 18.8 MB30.10 - Overfitting and Underfitting/30.10 - Overfitting and Underfitting.mp4 18.6 MB17.14 - TF-IDF (Code Sample)/17.14 - TF-IDF (Code Sample).mp4 18.5 MB37.3 - Core, Border and Noise points/37.3 - Core, Border and Noise points.mp4 18.5 MB18.19 - Weighted k-NN/18.19 - Weighted k-NN.mp4 18.5 MB45.16 - Logistic Regression without class balancing/45.16 - Logistic Regression without class balancing.mp4 18.4 MB26.6 - Learning rate/26.6 - Learning rate.mp4 18.3 MB56.1 - Problem definition/56.1 - Problem definition..mp4 18.2 MB4.6 - Modules/4.6 - Modules.mp4 18.2 MB11.2 - Population and Sample/11.2 - Population and Sample.mp4 18.2 MB56.18 - Weight features/56.18 - Weight features.mp4 18.0 MB50.19 - Assignment Try various CNN networks on MNIST dataset#/50.19 - Assignment Try various CNN networks on MNIST dataset..mp4 18.0 MB57.3 - Execution of an SQL statement/57.3 - Execution of an SQL statement..mp4 17.8 MB40.6 - Hamming loss/40.6 - Hamming loss.mp4 17.6 MB20.6 - Impact of outliers/20.6 - Impact of outliers.mp4 17.6 MB30.5 - Building a decision Tree Gini Impurity/30.5 - Building a decision Tree Gini Impurity.mp4 17.6 MB32.15 - AdaBoost geometric intuition/32.15 - AdaBoost geometric intuition.mp4 17.6 MB34.1 - Calibration of ModelsNeed for calibration/34.1 - Calibration of ModelsNeed for calibration.mp4 17.4 MB46.12 - Data Cleaning Fare/46.12 - Data Cleaning Fare.mp4 17.4 MB13.4 - How to represent a dataset as a Matrix/13.4 - How to represent a dataset as a Matrix..mp4 17.4 MB32.4 - Bias-Variance tradeoff/32.4 - Bias-Variance tradeoff.mp4 17.4 MB28.9 - Domain specific Kernels/28.9 - Domain specific Kernels.mp4 17.3 MB43.20 - Models on all features RandomForest and Xgboost/43.20 - Models on all features RandomForest and Xgboost.mp4 17.2 MB42.27 - Measuring goodness of our solution AB testing/42.27 - Measuring goodness of our solution AB testing.mp4 17.2 MB21.8 - Distribution of errors/21.8 - Distribution of errors.mp4 17.2 MB40.16 - Logistic regression revisited/40.16 - Logistic regression revisited.mp4 17.0 MB28.11 - nu-SVM control errors and support vectors/28.11 - nu-SVM control errors and support vectors.mp4 17.0 MB33.10 - Indicator variables/33.10 - Indicator variables.mp4 16.9 MB41.14 - ML Models Random Model/41.14 - ML Models Random Model.mp4 16.9 MB43.2 - Businessreal world problem Objectives and constraints/43.2 - Businessreal world problem Objectives and constraints.mp4 16.8 MB30.11 - Train and Run time complexity/30.11 - Train and Run time complexity.mp4 16.8 MB53.13 - Extensions/53.13 - Extensions..mp4 16.7 MB15.1 - What is t-SNE/15.1 - What is t-SNE.mp4 16.7 MB23.2 - Independent vs Mutually exclusive events/23.2 - Independent vs Mutually exclusive events.mp4 16.7 MB4.7 - Packages/4.7 - Packages.mp4 16.6 MB44.9 - Exploratory Data AnalysisAverage ratings for various slices/44.9 - Exploratory Data AnalysisAverage ratings for various slices.mp4 16.5 MB42.4 - Data folders and paths/42.4 - Data folders and paths.mp4 16.5 MB18.2 - Data matrix notation/18.2 - Data matrix notation.mp4 16.5 MB11.5 - Symmetric distribution, Skewness and Kurtosis/11.5 - Symmetric distribution, Skewness and Kurtosis.mp4 16.4 MB49.14 - Exercise Try different MLP architectures on MNIST dataset/49.14 - Exercise Try different MLP architectures on MNIST dataset..mp4 16.4 MB2.11 - Control flow break and continue/2.11 - Control flow break and continue.mp4 16.1 MB53.6 - EDA Steering angles/53.6 - EDA Steering angles.mp4 16.1 MB51.8 - Deep RNN/51.8 - Deep RNN..mp4 16.1 MB14.7 - Visualize MNIST dataset/14.7 - Visualize MNIST dataset.mp4 16.0 MB44.26 - Final models with all features and predictors/44.26 - Final models with all features and predictors..mp4 16.0 MB20.19 - Intuitive understanding of bias-variance/20.19 - Intuitive understanding of bias-variance..mp4 16.0 MB57.17 - Order of keywords#/57.17 - Order of keywords..mp4 15.9 MB44.24 - Matrix Factorization models using Surprise/44.24 - Matrix Factorization models using Surprise.mp4 15.7 MB40.18 - Assignments/40.18 - Assignments..mp4 15.6 MB46.23 - Results/46.23 - Results..mp4 15.6 MB46.21 - Weighted Moving average/46.21 - Weighted Moving average..mp4 15.5 MB15.2 - Neighborhood of a point, Embedding/15.2 - Neighborhood of a point, Embedding.mp4 15.4 MB20.21 - best and wrost case of algorithm/20.21 - best and wrost case of algorithm.mp4 15.3 MB9.16 - Exercise Perform EDA on Haberman dataset/9.16 - Exercise Perform EDA on Haberman dataset.mp4 15.2 MB26.12 - Assignment 6 Implement SGD for linear regression/26.12 - Assignment 6 Implement SGD for linear regression.mp4 15.1 MB9.6 - Univariate Analysis using PDF/9.6 - Univariate Analysis using PDF.mp4 15.1 MB2.8 - Control flow if else/2.8 - Control flow if else.mp4 15.0 MB32.13 - Train and Run time complexity/32.13 - Train and Run time complexity.mp4 14.9 MB18.3 - Classification vs Regression (examples)/18.3 - Classification vs Regression (examples).mp4 14.8 MB11.32 - Code Snippet K-S Test/11.32 - Code Snippet K-S Test.mp4 14.7 MB8.3 - Find elements common in two lists/8.3 - Find elements common in two lists.mp4 14.6 MB9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation).mp4 14.5 MB32.8 - Random Tree Cases/32.8 - Random Tree Cases.mp4 14.5 MB10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D).mp4 14.5 MB38.11 - Cold Start problem/38.11 - Cold Start problem.mp4 14.4 MB9.2 - 3D scatter plot/9.2 - 3D scatter plot.mp4 14.4 MB32.1 - What are ensembles/32.1 - What are ensembles.mp4 14.3 MB57.24 - DDLALTER ADD, MODIFY, DROP/57.24 - DDLALTER ADD, MODIFY, DROP.mp4 14.3 MB44.12 - Computing Similarity matricesMovie-Movie similarity/44.12 - Computing Similarity matricesMovie-Movie similarity.mp4 14.3 MB26.10 - Logistic regression formulation revisited/26.10 - Logistic regression formulation revisited.mp4 14.1 MB42.3 - Amazon product advertising API/42.3 - Amazon product advertising API.mp4 13.9 MB18.20 - Voronoi diagram/18.20 - Voronoi diagram.mp4 13.9 MB42.23 - Building a real world solution/42.23 - Building a real world solution.mp4 13.8 MB54.9 - Char-RNN with abc-notation Generate tabla music/54.9 - Char-RNN with abc-notation Generate tabla music.mp4 13.6 MB18.18 - k-NN for regression/18.18 - k-NN for regression.mp4 13.5 MB40.3 - Mapping to an ML problem Data overview/40.3 - Mapping to an ML problem Data overview.mp4 13.5 MB23.13 - Outliers/23.13 - Outliers.mp4 13.5 MB11.22 - Correlation vs Causation/11.22 - Correlation vs Causation.mp4 13.5 MB44.6 - Exploratory Data AnalysisTemporal Train-Test split/44.6 - Exploratory Data AnalysisTemporal Train-Test split..mp4 13.3 MB2.4 - comments, indentation and statements/2.4 - comments, indentation and statements.mp4 13.2 MB41.3 - Mapping to an ML problem Data overview/41.3 - Mapping to an ML problem Data overview.mp4 13.2 MB37.2 - MinPts and Eps Density/37.2 - MinPts and Eps Density.mp4 13.2 MB37.4 - Density edge and Density connected points/37.4 - Density edge and Density connected points..mp4 13.1 MB44.4 - Mapping to an ML problemML problem formulation/44.4 - Mapping to an ML problemML problem formulation.mp4 13.0 MB51.11 - Exercise Amazon Fine Food reviews LSTM model/51.11 - Exercise Amazon Fine Food reviews LSTM model..mp4 12.9 MB13.6 - Mean of a data matrix/13.6 - Mean of a data matrix.mp4 12.7 MB21.7 - Median absolute deviation (MAD)/21.7 - Median absolute deviation (MAD).mp4 12.7 MB36.5 - Limitations of Hierarchical Clustering/36.5 - Limitations of Hierarchical Clustering.mp4 12.5 MB37.9 - Code samples/37.9 - Code samples..mp4 12.5 MB44.27 - Comparison between various models/44.27 - Comparison between various models..mp4 12.2 MB41.2 - Business objectives and constraints/41.2 - Business objectives and constraints..mp4 12.1 MB37.1 - Density based clustering/37.1 - Density based clustering.mp4 12.1 MB53.3 - Data understanding & Analysis Files and folders/53.3 - Data understanding & Analysis Files and folders..mp4 12.0 MB46.13 - Data Cleaning Remove all outlierserroneous points/46.13 - Data Cleaning Remove all outlierserroneous points.mp4 11.9 MB36.6 - Code sample/36.6 - Code sample.mp4 11.9 MB57.25 - DDLDROP TABLE, TRUNCATE, DELETE/57.25 - DDLDROP TABLE, TRUNCATE, DELETE.mp4 11.8 MB46.17 - Data PreparationSmoothing time-series data cont/46.17 - Data PreparationSmoothing time-series data cont...mp4 11.6 MB11.6 - Standard normal variate (Z) and standardization/11.6 - Standard normal variate (Z) and standardization.mp4 11.6 MB10.9 - Square ,Rectangle/10.9 - Square ,Rectangle.mp4 11.5 MB41.5 - Mapping to an ML problem Train-test split/41.5 - Mapping to an ML problem Train-test split.mp4 11.5 MB43.5 - Machine Learning problem mapping Train and test splitting/43.5 - Machine Learning problem mapping Train and test splitting.mp4 11.4 MB55.3 - Data cleaning & preprocessing/55.3 - Data cleaning & preprocessing.mp4 11.4 MB24.10 - Column Standardization/24.10 - Column Standardization.mp4 11.4 MB53.7 - Mean Baseline model simple/53.7 - Mean Baseline model simple.mp4 11.4 MB20.1 - Introduction/20.1 - Introduction.mp4 11.4 MB3.3 - Tuples part-2/3.3 - Tuples part-2.mp4 11.2 MB10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D).mp4 11.2 MB45.5 - ML problem formulation Train, CV and Test data construction/45.5 - ML problem formulation Train, CV and Test data construction.mp4 11.1 MB35.11 - Determining the right K/35.11 - Determining the right K.mp4 11.0 MB44.16 - Data Sampling/44.16 - Data Sampling..mp4 11.0 MB46.16 - Data PreparationSmoothing time-series data/46.16 - Data PreparationSmoothing time-series data..mp4 10.9 MB41.17 - Assignments/41.17 - Assignments.mp4 10.9 MB40.2 - Business objectives and constraints/40.2 - Business objectives and constraints.mp4 10.9 MB44.10 - Exploratory Data AnalysisCold start problem/44.10 - Exploratory Data AnalysisCold start problem.mp4 10.7 MB56.12 - Shortest Path/56.12 - Shortest Path.mp4 10.7 MB46.25 - Linear regression/46.25 - Linear regression..mp4 10.5 MB30.8 - Feature standardization/30.8 - Feature standardization.mp4 10.3 MB33.13 - Mathematical transforms/33.13 - Mathematical transforms.mp4 10.2 MB44.22 - Xgboost + 13 features +Surprise baseline model/44.22 - Xgboost + 13 features +Surprise baseline model.mp4 10.1 MB44.3 - Mapping to an ML problemData overview/44.3 - Mapping to an ML problemData overview..mp4 10.1 MB43.17 - t-SNE analysis/43.17 - t-SNE analysis..mp4 10.1 MB17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample).mp4 10.0 MB41.4 - Mapping to an ML problem ML problem and performance metric/41.4 - Mapping to an ML problem ML problem and performance metric..mp4 10.0 MB14.8 - Limitations of PCA/14.8 - Limitations of PCA.mp4.mkv 9.9 MB33.7 - Deep learning features CNN/33.7 - Deep learning features CNN.mp4 9.8 MB43.21 - Assignments/43.21 - Assignments..mp4 9.8 MB14.1 - Why learn PCA/14.1 - Why learn PCA.mp4 9.8 MB36.4 - Time and Space Complexity/36.4 - Time and Space Complexity.mp4 9.6 MB33.16 - Domain specific featurizations/33.16 - Domain specific featurizations.mp4 9.1 MB41.11 - EDA Data Visualization T-SNE/41.11 - EDA Data Visualization T-SNE..mp4 9.1 MB42.11 - Stemming/42.11 - Stemming.mp4 9.1 MB43.16 - Univariate analysis/43.16 - Univariate analysis.mp4 9.0 MB43.19 - Models on all features t-SNE/43.19 - Models on all features t-SNE.mp4 9.0 MB20.8 - k distance/20.8 - k distance.mp4 8.9 MB13.2 - Row Vector and Column Vector/13.2 - Row Vector and Column Vector.mp4 8.7 MB55.8 - Exercise Build deeper LSTM models and hyper-param tune them/55.8 - Exercise Build deeper LSTM models and hyper-param tune them.mp4 8.7 MB43.6 - Exploratory Data Analysis Class distribution/43.6 - Exploratory Data Analysis Class distribution..mp4 8.6 MB10.4 - Projection and Unit Vector/10.4 - Projection and Unit Vector.mp4 8.6 MB46.27 - Xgboost Regression/46.27 - Xgboost Regression.mp4 8.5 MB35.2 - Unsupervised learning/35.2 - Unsupervised learning.mp4 8.5 MB9.13 - Violin Plots/9.13 - Violin Plots.mp4 8.3 MB35.13 - Time and space complexity/35.13 - Time and space complexity.mp4 8.2 MB37.8 - Time and Space Complexity/37.8 - Time and Space Complexity.mp4 8.1 MB53.14 - Assignment/53.14 - Assignment..mp4 7.9 MB23.14 - Missing values/23.14 - Missing values.mp4 7.6 MB38.5 - Matrix Factorization NMF/38.5 - Matrix Factorization NMF.mp4 7.4 MB46.11 - Data Cleaning Distance/46.11 - Data Cleaning Distance..mp4 7.2 MB40.12 - Train-Test Split/40.12 - Train-Test Split.mp4 7.1 MB40.17 - Why not use advanced techniques/40.17 - Why not use advanced techniques.mp4 7.0 MB57.6 - Load IMDB data/57.6 - Load IMDB data..mp4 6.9 MB44.19 - Data transformation for Surprise/44.19 - Data transformation for Surprise..mp4 6.8 MB23.17 - Similarity or Distance matrix/23.17 - Similarity or Distance matrix.mp4 6.7 MB43.9 - Exploratory Data Analysis Train-Test class distribution/43.9 - Exploratory Data Analysis Train-Test class distribution.mp4 6.4 MB10.1 - Why learn it/10.1 - Why learn it .mp4 6.3 MB10.10 - Hyper Cube,Hyper Cuboid/10.10 - Hyper Cube,Hyper Cuboid.mp4 6.2 MB13.3 - How to represent a data set/13.3 - How to represent a data set.mp4 5.6 MB53.5 - Split the dataset Train vs Test/53.5 - Split the dataset Train vs Test.mp4 5.6 MB23.18 - Large dimensionality/23.18 - Large dimensionality.mp4 5.5 MB2.2 - Why learn Python/2.2 - Why learn Python.mp4 5.4 MB13.1 - What is Dimensionality reduction/13.1 - What is Dimensionality reduction.mp4 4.8 MB43.15 - File-size feature/43.15 - File-size feature.mp4 4.7 MB44.17 - Google drive with intermediate files/44.17 - Google drive with intermediate files.mp4 4.7 MB23.16 - Multiclass classification/23.16 - Multiclass classification.mp4 4.6 MB
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How to initialize K-Means++/out.pdf 10.5 kB37.3 - Core, Border and Noise points/out.pdf 10.5 kB36.1 - Agglomerative & Divisive, Dendrograms/out.pdf 10.5 kB24.7 - Probabilistic Interpretation Gaussian Naive Bayes/out.pdf 10.5 kB41.17 - Assignments/out.pdf 10.5 kB21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/out.pdf 10.4 kB40.18 - Assignments/out.pdf 10.4 kB48.14 - Which algorithm to choose when/out.pdf 10.4 kB9.2 - 3D scatter plot/out.pdf 10.4 kB9.4 - Limitations of Pair Plots/out.pdf 10.4 kB9.5 - Histogram and Introduction to PDF(Probability Density Function)/out.pdf 10.4 kB9.6 - Univariate Analysis using PDF/out.pdf 10.4 kB9.8 - Mean, Variance and Standard Deviation/out.pdf 10.4 kB53.10 - NVIDIA’s end to end CNN model/out.pdf 10.4 kB24.14 - Real world cases/out.pdf 10.4 kB30.2 - Sample Decision tree/out.pdf 10.4 kB49.14 - Exercise Try different MLP architectures on MNIST dataset/out.pdf 10.4 kB47.12 - Vanishing Gradient problem/out.pdf 10.4 kB51.6 - LSTM/out.pdf 10.3 kB36.3 - Proximity methods Advantages and Limitations/out.pdf 10.3 kB36.4 - Time and Space Complexity/out.pdf 10.3 kB14.2 - Geometric intuition of PCA/out.pdf 10.3 kB4.1 - Introduction/out.pdf 10.3 kB36.2 - Agglomerative Clustering/out.pdf 10.3 kB54.11 - Survey blog/out.pdf 10.3 kB47.11 - Activation functions/out.pdf 10.3 kB48.21 - Word2Vec Algorithmic Optimizations/out.pdf 10.2 kB55.2 - Dataset understanding/out.pdf 10.2 kB8.1 - Space and Time Complexity Find largest number in a list/out.pdf 10.2 kB8.2 - Binary search/out.pdf 10.2 kB8.3 - Find elements common in two lists/out.pdf 10.2 kB8.4 - Find elements common in two lists using a HashtableDict/out.pdf 10.2 kB35.4 - Metrics for Clustering/out.pdf 10.2 kB54.1 - Real-world problem/out.pdf 10.1 kB50.9 - ImageNet dataset/out.pdf 10.1 kB50.7 - CNN Training Optimization/out.pdf 10.1 kB32.9 - Boosting Intuition/out.pdf 10.0 kB2.3 - Keywords and identifiers/out.pdf 10.0 kB42.10 - Text Pre-Processing Tokenization and Stop-word removal/out.pdf 10.0 kB42.11 - Stemming/out.pdf 10.0 kB42.12 - Text based product similarity Converting text to an n-D vector bag of words/out.pdf 10.0 kB42.13 - Code for bag of words based product similarity/out.pdf 10.0 kB42.14 - TF-IDF featurizing text based on word-importance/out.pdf 10.0 kB42.15 - Code for TF-IDF based product similarity/out.pdf 10.0 kB42.16 - Code for IDF based product similarity/out.pdf 10.0 kB42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/out.pdf 10.0 kB42.18 - Code for Average Word2Vec product similarity/out.pdf 10.0 kB42.2 - Plan of action/out.pdf 10.0 kB42.20 - Code for IDF weighted Word2Vec product similarity/out.pdf 10.0 kB42.21 - Weighted similarity using brand and color/out.pdf 10.0 kB42.22 - Code for weighted similarity/out.pdf 10.0 kB42.23 - Building a real world solution/out.pdf 10.0 kB42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/out.pdf 10.0 kB42.25 - Using Keras + Tensorflow to extract features/out.pdf 10.0 kB42.26 - Visual similarity based product similarity/out.pdf 10.0 kB42.27 - Measuring goodness of our solution AB testing/out.pdf 10.0 kB42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/out.pdf 10.0 kB42.3 - Amazon product advertising API/out.pdf 10.0 kB42.4 - Data folders and paths/out.pdf 10.0 kB42.5 - Overview of the data and Terminology/out.pdf 10.0 kB42.6 - Data cleaning and understandingMissing data in various features/out.pdf 10.0 kB42.7 - Understand duplicate rows/out.pdf 10.0 kB42.8 - Remove duplicates Part 1/out.pdf 10.0 kB42.9 - Remove duplicates Part 2/out.pdf 10.0 kB25.4 - Code sample for Linear Regression/out.pdf 10.0 kB7.1 - Getting started with pandas/out.pdf 10.0 kB7.2 - Data Frame Basics/out.pdf 10.0 kB11.13 - How to randomly sample data points (Uniform Distribution)/out.pdf 10.0 kB11.27 - Confidence interval using bootstrapping/out.pdf 10.0 kB11.32 - Code Snippet K-S Test/out.pdf 10.0 kB11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/out.pdf 10.0 kB13.1 - What is Dimensionality reduction/out.pdf 10.0 kB13.10 - Code to Load MNIST Data Set/out.pdf 10.0 kB13.3 - How to represent a data set/out.pdf 10.0 kB14.10 - PCA for dimensionality reduction (not-visualization)/out.pdf 10.0 kB2.4 - comments, indentation and statements/out.pdf 10.0 kB2.5 - Variables and data types in Python/out.pdf 10.0 kB2.6 - Standard Input and Output/out.pdf 10.0 kB2.7 - Operators/out.pdf 10.0 kB3.2 - Tuples part 1/out.pdf 10.0 kB3.3 - Tuples part-2/out.pdf 10.0 kB3.4 - Sets/out.pdf 10.0 kB3.5 - Dictionary/out.pdf 10.0 kB3.6 - Strings/out.pdf 10.0 kB4.10 - Debugging Python/out.pdf 10.0 kB4.2 - Types of functions/out.pdf 10.0 kB4.3 - Function arguments/out.pdf 10.0 kB4.4 - Recursive functions/out.pdf 10.0 kB4.5 - Lambda functions/out.pdf 10.0 kB4.8 - File Handling/out.pdf 10.0 kB4.9 - Exception Handling/out.pdf 10.0 kB9.1 - Introduction to IRIS dataset and 2D scatter plot/out.pdf 10.0 kB9.10 - Percentiles and Quantiles/out.pdf 10.0 kB9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/out.pdf 10.0 kB9.12 - Box-plot with Whiskers/out.pdf 10.0 kB9.13 - Violin Plots/out.pdf 10.0 kB9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/out.pdf 10.0 kB9.15 - Multivariate Probability Density, Contour Plot/out.pdf 10.0 kB9.9 - Median/out.pdf 10.0 kB49.5 - Online documentation and tutorials/out.pdf 10.0 kB46.29 - Assignment/out.pdf 10.0 kB50.17 - Code example Cats vs Dogs/out.pdf 10.0 kB10.3 - Dot Product and Angle between 2 Vectors/out.pdf 10.0 kB44.28 - Assignments/out.pdf 9.9 kB43.21 - Assignments/out.pdf 9.9 kB56.1 - Problem definition/out.pdf 9.9 kB43.14 - ASM Files Feature extraction & Multiprocessing/out.pdf 9.9 kB13.9 - MNIST dataset (784 dimensional)/out.pdf 9.9 kB54.9 - Char-RNN with abc-notation Generate tabla music/out.pdf 9.9 kB38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/out.pdf 9.8 kB48.18 - Auto Encoders/out.pdf 9.8 kB54.3 - Char-RNN with abc-notation Char-RNN model/out.pdf 9.8 kB34.9 - Retraining models periodically/out.pdf 9.7 kB48.3 - Rectified Linear Units (ReLU)/out.pdf 9.7 kB57.20 - Sub QueriesNested QueriesInner Queries/out.pdf 9.7 kB28.7 - Polynomial Kernel/out.pdf 9.7 kB57.27 - Learning resources/out.pdf 9.7 kB38.8 - Clustering as MF/out.pdf 9.7 kB50.10 - Data Augmentation/out.pdf 9.5 kB34.8 - Productionizing models/out.pdf 9.5 kB48.5 - Batch Normalization/out.pdf 9.5 kB34.7 - Modeling in the presence of outliers RANSAC/out.pdf 9.5 kB25.3 - Real world Cases/out.pdf 9.5 kB34.3 - Calibration Plots/out.pdf 9.4 kB23.4 - Exercise problems on Bayes Theorem/out.pdf 9.4 kB34.5 - Isotonic Regression/out.pdf 9.4 kB34.12 - VC dimension/out.pdf 9.4 kB15.5 - How to apply t-SNE and interpret its output/out.pdf 9.4 kB49.3 - Google Colaboratory/out.pdf 9.3 kB20.19 - Intuitive understanding of bias-variance/out.pdf 9.3 kB34.4 - Platt’s CalibrationScaling/out.pdf 9.2 kB58.1 - AD-Click Predicition/out_files/css(1) 9.2 kB41.1 - BusinessReal world problem Problem definition/out.pdf 8.9 kB41.10 - EDA Feature analysis/out.pdf 8.9 kB41.11 - EDA Data Visualization T-SNE/out.pdf 8.9 kB41.12 - EDA TF-IDF weighted Word2Vec featurization/out.pdf 8.9 kB41.13 - ML Models Loading Data/out.pdf 8.9 kB41.14 - ML Models Random Model/out.pdf 8.9 kB41.15 - ML Models Logistic Regression and Linear SVM/out.pdf 8.9 kB41.16 - ML Models XGBoost/out.pdf 8.9 kB41.2 - Business objectives and constraints/out.pdf 8.9 kB41.3 - Mapping to an ML problem Data overview/out.pdf 8.9 kB41.4 - Mapping to an ML problem ML problem and performance metric/out.pdf 8.9 kB41.5 - Mapping to an ML problem Train-test split/out.pdf 8.9 kB41.6 - EDA Basic Statistics/out.pdf 8.9 kB41.7 - EDA Basic Feature Extraction/out.pdf 8.9 kB41.8 - EDA Text Preprocessing/out.pdf 8.9 kB54.8 - Char-RNN with abc-notation Music generation/out.pdf 8.9 kB10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/out.pdf 8.9 kB53.3 - Data understanding & Analysis Files and folders/out.pdf 8.9 kB42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/out.pdf 8.9 kB48.7 - OptimizersHill descent in 3D and contours/out.pdf 8.9 kB56.6 - EDABasic Stats/out.pdf 8.8 kB24.18 - Extensions to Generalized linear models/out.pdf 8.8 kB38.1 - Problem formulation Movie reviews/out.pdf 8.7 kB55.1 - Human Activity Recognition Problem definition/out.pdf 8.7 kB44.17 - Google drive with intermediate files/out.pdf 8.6 kB46.1 - BusinessReal world problem Overview/out.pdf 8.6 kB46.14 - Data PreparationClusteringSegmentation/out.pdf 8.6 kB46.2 - Objectives and Constraints/out.pdf 8.6 kB46.10 - Data Cleaning Speed/out.pdf 8.6 kB46.11 - Data Cleaning Distance/out.pdf 8.6 kB46.12 - Data Cleaning Fare/out.pdf 8.6 kB46.13 - Data Cleaning Remove all outlierserroneous points/out.pdf 8.6 kB46.15 - Data PreparationTime binning/out.pdf 8.6 kB46.16 - Data PreparationSmoothing time-series data/out.pdf 8.6 kB46.19 - Ratios and previous-time-bin values/out.pdf 8.6 kB46.20 - Simple moving average/out.pdf 8.6 kB46.21 - Weighted Moving average/out.pdf 8.6 kB46.22 - Exponential weighted moving average/out.pdf 8.6 kB46.24 - Regression models Train-Test split & Features/out.pdf 8.6 kB46.25 - Linear regression/out.pdf 8.6 kB46.26 - Random Forest regression/out.pdf 8.6 kB46.27 - Xgboost Regression/out.pdf 8.6 kB46.28 - Model comparison/out.pdf 8.6 kB46.5 - Mapping to ML problem FieldsFeatures/out.pdf 8.6 kB46.6 - Mapping to ML problem Time series forecastingRegression/out.pdf 8.6 kB46.7 - Mapping to ML problem Performance metrics/out.pdf 8.6 kB46.8 - Data Cleaning Latitude and Longitude data/out.pdf 8.6 kB46.9 - Data Cleaning Trip Duration/out.pdf 8.6 kB49.6 - Softmax Classifier on MNIST dataset/out.pdf 8.6 kB46.4 - Mapping to ML problem dask dataframes/out.pdf 8.6 kB40.1 - BusinessReal world problem/out.pdf 8.6 kB40.10 - Data Modeling Multi label Classification/out.pdf 8.6 kB40.11 - Data preparation/out.pdf 8.6 kB40.12 - Train-Test Split/out.pdf 8.6 kB40.13 - Featurization/out.pdf 8.6 kB40.14 - Logistic regression One VS Rest/out.pdf 8.6 kB40.15 - Sampling data and tags+Weighted models/out.pdf 8.6 kB40.16 - Logistic regression revisited/out.pdf 8.6 kB40.17 - Why not use advanced techniques/out.pdf 8.6 kB40.2 - Business objectives and constraints/out.pdf 8.6 kB40.3 - Mapping to an ML problem Data overview/out.pdf 8.6 kB40.4 - Mapping to an ML problemML problem formulation/out.pdf 8.6 kB40.5 - Mapping to an ML problemPerformance metrics/out.pdf 8.6 kB40.6 - Hamming loss/out.pdf 8.6 kB40.7 - EDAData Loading/out.pdf 8.6 kB40.8 - EDAAnalysis of tags/out.pdf 8.6 kB40.9 - EDAData Preprocessing/out.pdf 8.6 kB44.1 - BusinessReal world problemProblem definition/out.pdf 8.6 kB44.10 - Exploratory Data AnalysisCold start problem/out.pdf 8.6 kB44.11 - Computing Similarity matricesUser-User similarity matrix/out.pdf 8.6 kB44.12 - Computing Similarity matricesMovie-Movie similarity/out.pdf 8.6 kB44.13 - Computing Similarity matricesDoes movie-movie similarity work/out.pdf 8.6 kB44.15 - Overview of the modelling strategy/out.pdf 8.6 kB44.18 - Featurizations for regression/out.pdf 8.6 kB44.19 - Data transformation for Surprise/out.pdf 8.6 kB44.2 - Objectives and constraints/out.pdf 8.6 kB44.20 - Xgboost with 13 features/out.pdf 8.6 kB44.22 - Xgboost + 13 features +Surprise baseline model/out.pdf 8.6 kB44.23 - Surprise KNN predictors/out.pdf 8.6 kB44.24 - Matrix Factorization models using Surprise/out.pdf 8.6 kB44.25 - SVD ++ with implicit feedback/out.pdf 8.6 kB44.26 - Final models with all features and predictors/out.pdf 8.6 kB44.27 - Comparison between various models/out.pdf 8.6 kB44.3 - Mapping to an ML problemData overview/out.pdf 8.6 kB44.4 - Mapping to an ML problemML problem formulation/out.pdf 8.6 kB44.5 - Exploratory Data AnalysisData preprocessing/out.pdf 8.6 kB44.6 - Exploratory Data AnalysisTemporal Train-Test split/out.pdf 8.6 kB44.7 - Exploratory Data AnalysisPreliminary data analysis/out.pdf 8.6 kB44.9 - Exploratory Data AnalysisAverage ratings for various slices/out.pdf 8.6 kB44.16 - Data Sampling/out.pdf 8.6 kB49.12 - MNIST classification in Keras/out.pdf 8.6 kB49.13 - Hyperparameter tuning in Keras/out.pdf 8.6 kB49.7 - MLP Initialization/out.pdf 8.6 kB45.1 - BusinessReal world problem Overview/out.pdf 8.6 kB45.11 - Univariate AnalysisText feature/out.pdf 8.6 kB45.12 - Machine Learning ModelsData preparation/out.pdf 8.6 kB45.13 - Baseline Model Naive Bayes/out.pdf 8.6 kB45.15 - Logistic Regression with class balancing/out.pdf 8.6 kB45.16 - Logistic Regression without class balancing/out.pdf 8.6 kB45.17 - Linear-SVM/out.pdf 8.6 kB45.18 - Random-Forest with one-hot encoded features/out.pdf 8.6 kB45.19 - Random-Forest with response-coded features/out.pdf 8.6 kB45.2 - Business objectives and constraints/out.pdf 8.6 kB45.20 - Stacking Classifier/out.pdf 8.6 kB45.21 - Majority Voting classifier/out.pdf 8.6 kB45.22 - Assignments/out.pdf 8.6 kB45.3 - ML problem formulation Data/out.pdf 8.6 kB45.4 - ML problem formulation Mapping real world to ML problem/out.pdf 8.6 kB45.5 - ML problem formulation Train, CV and Test data construction/out.pdf 8.6 kB45.6 - Exploratory Data AnalysisReading data & preprocessing/out.pdf 8.6 kB45.7 - Exploratory Data AnalysisDistribution of Class-labels/out.pdf 8.6 kB45.8 - Exploratory Data Analysis “Random” Model/out.pdf 8.6 kB45.9 - Univariate AnalysisGene feature/out.pdf 8.6 kB46.3 - Mapping to ML problem Data/out.pdf 8.6 kB43.1 - Businessreal world problem Problem definition/out.pdf 8.6 kB43.10 - ML models – using byte files only Random Model/out.pdf 8.6 kB43.11 - k-NN/out.pdf 8.6 kB43.12 - Logistic regression/out.pdf 8.6 kB43.13 - Random Forest and Xgboost/out.pdf 8.6 kB43.15 - File-size feature/out.pdf 8.6 kB43.16 - Univariate analysis/out.pdf 8.6 kB43.17 - t-SNE analysis/out.pdf 8.6 kB43.18 - ML models on ASM file features/out.pdf 8.6 kB43.19 - Models on all features t-SNE/out.pdf 8.6 kB43.2 - Businessreal world problem Objectives and constraints/out.pdf 8.6 kB43.20 - Models on all features RandomForest and Xgboost/out.pdf 8.6 kB43.3 - Machine Learning problem mapping Data overview/out.pdf 8.6 kB43.4 - Machine Learning problem mapping ML problem/out.pdf 8.6 kB43.5 - Machine Learning problem mapping Train and test splitting/out.pdf 8.6 kB43.6 - Exploratory Data Analysis Class distribution/out.pdf 8.6 kB43.7 - Exploratory Data Analysis Feature extraction from byte files/out.pdf 8.6 kB43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/out.pdf 8.6 kB43.9 - Exploratory Data Analysis Train-Test class distribution/out.pdf 8.6 kB53.13 - Extensions/out.pdf 8.6 kB49.4 - Install TensorFlow/out.pdf 8.5 kB53.11 - Train the model/out.pdf 8.5 kB47.14 - Decision surfaces Playground/out.pdf 8.5 kB53.12 - Test and visualize the output/out.pdf 8.3 kB24.5 - L2 Regularization Overfitting and Underfitting/out.pdf 6.9 kB58.1 - AD-Click Predicition/out_files/smooth_scroll.min.js.pagespeed.jm.F46b1fzWC9.js.download 6.7 kB58.1 - AD-Click Predicition/out_files/wp-content,_plugins,_livemesh-siteorigin-widgets.download 6.5 kB58.1 - AD-Click Predicition/out_files/191x70xai-logo2.png.pagespeed.ic.tQcj-DGwlZ.webp 5.4 kB58.1 - AD-Click Predicition/out_files/A.jquery.scrollbar.css.pagespeed.cf.cKaYxTj1_t.css 5.0 kB58.1 - AD-Click Predicition/out_files/css(2) 4.9 kB58.1 - 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1.1 - How to Learn from Appliedaicourse/[FTU Forum].url 1.4 kB1.2 - How the Job Guarantee program works/[FTU Forum].url 1.4 kB10.1 - Why learn it/[FTU Forum].url 1.4 kB10.10 - Hyper Cube,Hyper Cuboid/[FTU Forum].url 1.4 kB10.11 - Revision Questions/[FTU Forum].url 1.4 kB10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/[FTU Forum].url 1.4 kB10.3 - Dot Product and Angle between 2 Vectors/[FTU Forum].url 1.4 kB10.4 - Projection and Unit Vector/[FTU Forum].url 1.4 kB10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/[FTU Forum].url 1.4 kB10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/[FTU Forum].url 1.4 kB10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/[FTU Forum].url 1.4 kB10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/[FTU Forum].url 1.4 kB10.9 - Square ,Rectangle/[FTU Forum].url 1.4 kB11.1 - Introduction to Probability and Statistics/[FTU Forum].url 1.4 kB11.10 - How distributions are used/[FTU Forum].url 1.4 kB11.11 - Chebyshev’s inequality/[FTU Forum].url 1.4 kB11.12 - Discrete and Continuous Uniform distributions/[FTU Forum].url 1.4 kB11.13 - How to randomly sample data points (Uniform Distribution)/[FTU Forum].url 1.4 kB11.14 - Bernoulli and Binomial Distribution/[FTU Forum].url 1.4 kB11.15 - Log Normal Distribution/[FTU Forum].url 1.4 kB11.16 - Power law distribution/[FTU Forum].url 1.4 kB11.17 - Box cox transform/[FTU Forum].url 1.4 kB11.18 - Applications of non-gaussian distributions/[FTU Forum].url 1.4 kB11.19 - Co-variance/[FTU Forum].url 1.4 kB11.2 - Population and Sample/[FTU Forum].url 1.4 kB11.20 - Pearson Correlation Coefficient/[FTU Forum].url 1.4 kB11.21 - Spearman Rank Correlation Coefficient/[FTU Forum].url 1.4 kB11.22 - Correlation vs Causation/[FTU Forum].url 1.4 kB11.23 - How to use correlations/[FTU Forum].url 1.4 kB11.24 - Confidence interval (C.I) Introduction/[FTU Forum].url 1.4 kB11.25 - Computing confidence interval given the underlying distribution/[FTU Forum].url 1.4 kB11.26 - C.I for mean of a normal random variable/[FTU Forum].url 1.4 kB11.27 - Confidence interval using bootstrapping/[FTU Forum].url 1.4 kB11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/[FTU Forum].url 1.4 kB11.29 - Hypothesis Testing Intution with coin toss example/[FTU Forum].url 1.4 kB11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/[FTU Forum].url 1.4 kB11.30 - Resampling and permutation test/[FTU Forum].url 1.4 kB11.31 - K-S Test for similarity of two distributions/[FTU Forum].url 1.4 kB11.32 - Code Snippet K-S Test/[FTU Forum].url 1.4 kB11.33 - Hypothesis testing another example/[FTU Forum].url 1.4 kB11.34 - Resampling and Permutation test another example/[FTU Forum].url 1.4 kB11.35 - How to use hypothesis testing/[FTU Forum].url 1.4 kB11.36 - Proportional Sampling/[FTU Forum].url 1.4 kB11.37 - Revision Questions/[FTU Forum].url 1.4 kB11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/[FTU Forum].url 1.4 kB11.5 - Symmetric distribution, Skewness and Kurtosis/[FTU Forum].url 1.4 kB11.6 - Standard normal variate (Z) and standardization/[FTU Forum].url 1.4 kB11.7 - Kernel density estimation/[FTU Forum].url 1.4 kB11.8 - Sampling distribution & Central Limit theorem/[FTU Forum].url 1.4 kB11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/[FTU Forum].url 1.4 kB12.1 - Questions & Answers/[FTU Forum].url 1.4 kB13.1 - What is Dimensionality reduction/[FTU Forum].url 1.4 kB13.10 - Code to Load MNIST Data Set/[FTU Forum].url 1.4 kB13.2 - Row Vector and Column Vector/[FTU Forum].url 1.4 kB13.3 - How to represent a data set/[FTU Forum].url 1.4 kB13.4 - How to represent a dataset as a Matrix/[FTU Forum].url 1.4 kB13.5 - Data Preprocessing Feature Normalisation/[FTU Forum].url 1.4 kB13.6 - Mean of a data matrix/[FTU Forum].url 1.4 kB13.7 - Data Preprocessing Column Standardization/[FTU Forum].url 1.4 kB13.8 - Co-variance of a Data Matrix/[FTU Forum].url 1.4 kB13.9 - MNIST dataset (784 dimensional)/[FTU Forum].url 1.4 kB14.1 - Why learn PCA/[FTU Forum].url 1.4 kB14.10 - PCA for dimensionality reduction (not-visualization)/[FTU Forum].url 1.4 kB14.2 - Geometric intuition of PCA/[FTU Forum].url 1.4 kB14.3 - Mathematical objective function of PCA/[FTU Forum].url 1.4 kB14.4 - Alternative formulation of PCA Distance minimization/[FTU Forum].url 1.4 kB14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/[FTU Forum].url 1.4 kB14.6 - PCA for Dimensionality Reduction and Visualization/[FTU Forum].url 1.4 kB14.7 - Visualize MNIST dataset/[FTU Forum].url 1.4 kB14.8 - Limitations of PCA/[FTU Forum].url 1.4 kB14.9 - PCA Code example/[FTU Forum].url 1.4 kB15.1 - What is t-SNE/[FTU Forum].url 1.4 kB15.2 - Neighborhood of a point, Embedding/[FTU Forum].url 1.4 kB15.3 - Geometric intuition of t-SNE/[FTU Forum].url 1.4 kB15.4 - Crowding Problem/[FTU Forum].url 1.4 kB15.5 - How to apply t-SNE and interpret its output/[FTU Forum].url 1.4 kB15.6 - t-SNE on MNIST/[FTU Forum].url 1.4 kB15.7 - Code example of t-SNE/[FTU Forum].url 1.4 kB15.8 - Revision Questions/[FTU Forum].url 1.4 kB16.1 - Questions & Answers/[FTU Forum].url 1.4 kB17.1 - Dataset overview Amazon Fine Food reviews(EDA)/[FTU Forum].url 1.4 kB17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/[FTU Forum].url 1.4 kB17.11 - Bag of Words( Code Sample)/[FTU Forum].url 1.4 kB17.12 - Text Preprocessing( Code Sample)/[FTU Forum].url 1.4 kB17.13 - Bi-Grams and n-grams (Code Sample)/[FTU Forum].url 1.4 kB17.14 - TF-IDF (Code Sample)/[FTU Forum].url 1.4 kB17.15 - Word2Vec (Code Sample)/[FTU Forum].url 1.4 kB17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/[FTU Forum].url 1.4 kB17.17 - Assignment-2 Apply t-SNE/[FTU Forum].url 1.4 kB17.2 - Data Cleaning Deduplication/[FTU Forum].url 1.4 kB17.3 - Why convert text to a vector/[FTU Forum].url 1.4 kB17.4 - Bag of Words (BoW)/[FTU Forum].url 1.4 kB17.5 - Text Preprocessing Stemming/[FTU Forum].url 1.4 kB17.6 - uni-gram, bi-gram, n-grams/[FTU Forum].url 1.4 kB17.7 - tf-idf (term frequency- inverse document frequency)/[FTU Forum].url 1.4 kB17.8 - Why use log in IDF/[FTU Forum].url 1.4 kB17.9 - Word2Vec/[FTU Forum].url 1.4 kB18.1 - How “Classification” works/[FTU Forum].url 1.4 kB18.10 - KNN Limitations/[FTU Forum].url 1.4 kB18.11 - Decision surface for K-NN as K changes/[FTU Forum].url 1.4 kB18.12 - Overfitting and Underfitting/[FTU Forum].url 1.4 kB18.13 - Need for Cross validation/[FTU Forum].url 1.4 kB18.14 - K-fold cross validation/[FTU Forum].url 1.4 kB18.15 - Visualizing train, validation and test datasets/[FTU Forum].url 1.4 kB18.16 - How to determine overfitting and underfitting/[FTU Forum].url 1.4 kB18.17 - Time based splitting/[FTU Forum].url 1.4 kB18.18 - k-NN for regression/[FTU Forum].url 1.4 kB18.19 - Weighted k-NN/[FTU Forum].url 1.4 kB18.2 - Data matrix notation/[FTU Forum].url 1.4 kB18.20 - Voronoi diagram/[FTU Forum].url 1.4 kB18.21 - Binary search tree/[FTU Forum].url 1.4 kB18.22 - How to build a kd-tree/[FTU Forum].url 1.4 kB18.23 - Find nearest neighbours using kd-tree/[FTU Forum].url 1.4 kB18.24 - Limitations of Kd tree/[FTU Forum].url 1.4 kB18.25 - Extensions/[FTU Forum].url 1.4 kB18.26 - Hashing vs LSH/[FTU Forum].url 1.4 kB18.27 - LSH for cosine similarity/[FTU Forum].url 1.4 kB18.28 - LSH for euclidean distance/[FTU Forum].url 1.4 kB18.29 - Probabilistic class label/[FTU Forum].url 1.4 kB18.3 - Classification vs Regression (examples)/[FTU Forum].url 1.4 kB18.30 - Code SampleDecision boundary/[FTU Forum].url 1.4 kB18.31 - Code SampleCross Validation/[FTU Forum].url 1.4 kB18.32 - Revision Questions/[FTU Forum].url 1.4 kB18.4 - K-Nearest Neighbours Geometric intuition with a toy example/[FTU Forum].url 1.4 kB18.5 - Failure cases of KNN/[FTU Forum].url 1.4 kB18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/[FTU Forum].url 1.4 kB18.7 - Cosine Distance & Cosine Similarity/[FTU Forum].url 1.4 kB18.8 - How to measure the effectiveness of k-NN/[FTU Forum].url 1.4 kB18.9 - TestEvaluation time and space complexity/[FTU Forum].url 1.4 kB19.1 - Questions & Answers/[FTU Forum].url 1.4 kB2.1 - Python, Anaconda and relevant packages installations/[FTU Forum].url 1.4 kB2.10 - Control flow for loop/[FTU Forum].url 1.4 kB2.11 - Control flow break and continue/[FTU Forum].url 1.4 kB2.2 - Why learn Python/[FTU Forum].url 1.4 kB2.3 - Keywords and identifiers/[FTU Forum].url 1.4 kB2.4 - comments, indentation and statements/[FTU Forum].url 1.4 kB2.5 - Variables and data types in Python/[FTU Forum].url 1.4 kB2.6 - Standard Input and Output/[FTU Forum].url 1.4 kB2.7 - Operators/[FTU Forum].url 1.4 kB2.8 - Control flow if else/[FTU Forum].url 1.4 kB2.9 - Control flow while loop/[FTU Forum].url 1.4 kB20.1 - Introduction/[FTU Forum].url 1.4 kB20.10 - Local reachability-density(A)/[FTU Forum].url 1.4 kB20.11 - Local outlier Factor(A)/[FTU Forum].url 1.4 kB20.12 - Impact of Scale & Column standardization/[FTU Forum].url 1.4 kB20.13 - Interpretability/[FTU Forum].url 1.4 kB20.14 - Feature Importance and Forward Feature selection/[FTU Forum].url 1.4 kB20.15 - Handling categorical and numerical features/[FTU Forum].url 1.4 kB20.16 - Handling missing values by imputation/[FTU Forum].url 1.4 kB20.17 - curse of dimensionality/[FTU Forum].url 1.4 kB20.18 - Bias-Variance tradeoff/[FTU Forum].url 1.4 kB20.19 - Intuitive understanding of bias-variance/[FTU Forum].url 1.4 kB20.2 - Imbalanced vs balanced dataset/[FTU Forum].url 1.4 kB20.20 - Revision Questions/[FTU Forum].url 1.4 kB20.21 - best and wrost case of algorithm/[FTU Forum].url 1.4 kB20.3 - Multi-class classification/[FTU Forum].url 1.4 kB20.4 - k-NN, given a distance or similarity matrix/[FTU Forum].url 1.4 kB20.5 - Train and test set differences/[FTU Forum].url 1.4 kB20.6 - Impact of outliers/[FTU Forum].url 1.4 kB20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/[FTU Forum].url 1.4 kB20.8 - k distance/[FTU Forum].url 1.4 kB20.9 - Reachability-Distance(A,B)/[FTU Forum].url 1.4 kB21.1 - Accuracy/[FTU Forum].url 1.4 kB21.10 - Revision Questions/[FTU Forum].url 1.4 kB21.2 - Confusion matrix, TPR, FPR, FNR, TNR/[FTU Forum].url 1.4 kB21.3 - Precision and recall, F1-score/[FTU Forum].url 1.4 kB21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/[FTU Forum].url 1.4 kB21.5 - Log-loss/[FTU Forum].url 1.4 kB21.6 - R-SquaredCoefficient of determination/[FTU Forum].url 1.4 kB21.7 - Median absolute deviation (MAD)/[FTU Forum].url 1.4 kB21.8 - Distribution of errors/[FTU Forum].url 1.4 kB21.9 - Assignment-3 Apply k-Nearest Neighbor/[FTU Forum].url 1.4 kB22.1 - Questions & Answers/[FTU Forum].url 1.4 kB23.1 - Conditional probability/[FTU Forum].url 1.4 kB23.10 - Bias and Variance tradeoff/[FTU Forum].url 1.4 kB23.11 - Feature importance and interpretability/[FTU Forum].url 1.4 kB23.12 - Imbalanced data/[FTU Forum].url 1.4 kB23.13 - Outliers/[FTU Forum].url 1.4 kB23.14 - Missing values/[FTU Forum].url 1.4 kB23.15 - Handling Numerical features (Gaussian NB)/[FTU Forum].url 1.4 kB23.16 - Multiclass classification/[FTU Forum].url 1.4 kB23.17 - Similarity or Distance matrix/[FTU Forum].url 1.4 kB23.18 - Large dimensionality/[FTU Forum].url 1.4 kB23.19 - Best and worst cases/[FTU Forum].url 1.4 kB23.2 - Independent vs Mutually exclusive events/[FTU Forum].url 1.4 kB23.20 - Code example/[FTU Forum].url 1.4 kB23.21 - Assignment-4 Apply Naive Bayes/[FTU Forum].url 1.4 kB23.22 - Revision Questions/[FTU Forum].url 1.4 kB23.3 - Bayes Theorem with examples/[FTU Forum].url 1.4 kB23.4 - Exercise problems on Bayes Theorem/[FTU Forum].url 1.4 kB23.5 - Naive Bayes algorithm/[FTU Forum].url 1.4 kB23.6 - Toy example Train and test stages/[FTU Forum].url 1.4 kB23.7 - Naive Bayes on Text data/[FTU Forum].url 1.4 kB23.8 - LaplaceAdditive Smoothing/[FTU Forum].url 1.4 kB23.9 - Log-probabilities for numerical stability/[FTU Forum].url 1.4 kB24.1 - Geometric intuition of Logistic Regression/[FTU Forum].url 1.4 kB24.10 - Column Standardization/[FTU Forum].url 1.4 kB24.11 - Feature importance and Model interpretability/[FTU Forum].url 1.4 kB24.12 - Collinearity of features/[FTU Forum].url 1.4 kB24.13 - TestRun time space and time complexity/[FTU Forum].url 1.4 kB24.14 - Real world cases/[FTU Forum].url 1.4 kB24.15 - Non-linearly separable data & feature engineering/[FTU Forum].url 1.4 kB24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/[FTU Forum].url 1.4 kB24.17 - Assignment-5 Apply Logistic Regression/[FTU Forum].url 1.4 kB24.18 - Extensions to Generalized linear models/[FTU Forum].url 1.4 kB24.2 - Sigmoid function Squashing/[FTU Forum].url 1.4 kB24.3 - Mathematical formulation of Objective function/[FTU Forum].url 1.4 kB24.4 - Weight vector/[FTU Forum].url 1.4 kB24.5 - L2 Regularization Overfitting and Underfitting/[FTU Forum].url 1.4 kB24.6 - L1 regularization and sparsity/[FTU Forum].url 1.4 kB24.7 - Probabilistic Interpretation Gaussian Naive Bayes/[FTU Forum].url 1.4 kB24.8 - Loss minimization interpretation/[FTU Forum].url 1.4 kB24.9 - hyperparameters and random search/[FTU Forum].url 1.4 kB25.1 - Geometric intuition of Linear Regression/[FTU Forum].url 1.4 kB25.2 - Mathematical formulation/[FTU Forum].url 1.4 kB25.3 - Real world Cases/[FTU Forum].url 1.4 kB25.4 - Code sample for Linear Regression/[FTU Forum].url 1.4 kB26.1 - Differentiation/[FTU Forum].url 1.4 kB26.10 - Logistic regression formulation revisited/[FTU Forum].url 1.4 kB26.11 - Why L1 regularization creates sparsity/[FTU Forum].url 1.4 kB26.12 - Assignment 6 Implement SGD for linear regression/[FTU Forum].url 1.4 kB26.13 - Revision questions/[FTU Forum].url 1.4 kB26.2 - Online differentiation tools/[FTU Forum].url 1.4 kB26.3 - Maxima and Minima/[FTU Forum].url 1.4 kB26.4 - Vector calculus Grad/[FTU Forum].url 1.4 kB26.5 - Gradient descent geometric intuition/[FTU Forum].url 1.4 kB26.6 - Learning rate/[FTU Forum].url 1.4 kB26.7 - Gradient descent for linear regression/[FTU Forum].url 1.4 kB26.8 - SGD algorithm/[FTU Forum].url 1.4 kB26.9 - Constrained Optimization & PCA/[FTU Forum].url 1.4 kB27.1 - Questions & Answers/[FTU Forum].url 1.4 kB28.1 - Geometric Intution/[FTU Forum].url 1.4 kB28.10 - Train and run time complexities/[FTU Forum].url 1.4 kB28.11 - nu-SVM control errors and support vectors/[FTU Forum].url 1.4 kB28.12 - SVM Regression/[FTU Forum].url 1.4 kB28.13 - Cases/[FTU Forum].url 1.4 kB28.14 - Code Sample/[FTU Forum].url 1.4 kB28.15 - Assignment-7 Apply SVM/[FTU Forum].url 1.4 kB28.16 - Revision Questions/[FTU Forum].url 1.4 kB28.2 - Mathematical derivation/[FTU Forum].url 1.4 kB28.3 - Why we take values +1 and and -1 for Support vector planes/[FTU Forum].url 1.4 kB28.4 - Loss function (Hinge Loss) based interpretation/[FTU Forum].url 1.4 kB28.5 - Dual form of SVM formulation/[FTU Forum].url 1.4 kB28.6 - kernel trick/[FTU Forum].url 1.4 kB28.7 - Polynomial Kernel/[FTU Forum].url 1.4 kB28.8 - RBF-Kernel/[FTU Forum].url 1.4 kB28.9 - Domain specific Kernels/[FTU Forum].url 1.4 kB29.1 - Questions & Answers/[FTU Forum].url 1.4 kB3.1 - Lists/[FTU Forum].url 1.4 kB3.2 - Tuples part 1/[FTU Forum].url 1.4 kB3.3 - Tuples part-2/[FTU Forum].url 1.4 kB3.4 - Sets/[FTU Forum].url 1.4 kB3.5 - Dictionary/[FTU Forum].url 1.4 kB3.6 - Strings/[FTU Forum].url 1.4 kB30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/[FTU Forum].url 1.4 kB30.10 - Overfitting and Underfitting/[FTU Forum].url 1.4 kB30.11 - Train and Run time complexity/[FTU Forum].url 1.4 kB30.12 - Regression using Decision Trees/[FTU Forum].url 1.4 kB30.13 - Cases/[FTU Forum].url 1.4 kB30.14 - Code Samples/[FTU Forum].url 1.4 kB30.15 - Assignment-8 Apply Decision Trees/[FTU Forum].url 1.4 kB30.16 - Revision Questions/[FTU Forum].url 1.4 kB30.2 - Sample Decision tree/[FTU Forum].url 1.4 kB30.3 - Building a decision TreeEntropy/[FTU Forum].url 1.4 kB30.4 - Building a decision TreeInformation Gain/[FTU Forum].url 1.4 kB30.5 - Building a decision Tree Gini Impurity/[FTU Forum].url 1.4 kB30.6 - Building a decision Tree Constructing a DT/[FTU Forum].url 1.4 kB30.7 - Building a decision Tree Splitting numerical features/[FTU Forum].url 1.4 kB30.8 - Feature standardization/[FTU Forum].url 1.4 kB30.9 - Building a decision TreeCategorical features with many possible values/[FTU Forum].url 1.4 kB31.1 - Questions & Answers/[FTU Forum].url 1.4 kB32.1 - What are ensembles/[FTU Forum].url 1.4 kB32.10 - Residuals, Loss functions and gradients/[FTU Forum].url 1.4 kB32.11 - Gradient Boosting/[FTU Forum].url 1.4 kB32.12 - Regularization by Shrinkage/[FTU Forum].url 1.4 kB32.13 - Train and Run time complexity/[FTU Forum].url 1.4 kB32.14 - XGBoost Boosting + Randomization/[FTU Forum].url 1.4 kB32.15 - AdaBoost geometric intuition/[FTU Forum].url 1.4 kB32.16 - Stacking models/[FTU Forum].url 1.4 kB32.17 - Cascading classifiers/[FTU Forum].url 1.4 kB32.18 - Kaggle competitions vs Real world/[FTU Forum].url 1.4 kB32.19 - Assignment-9 Apply Random Forests & GBDT/[FTU Forum].url 1.4 kB32.2 - Bootstrapped Aggregation (Bagging) Intuition/[FTU Forum].url 1.4 kB32.20 - Revision Questions/[FTU Forum].url 1.4 kB32.3 - Random Forest and their construction/[FTU Forum].url 1.4 kB32.4 - Bias-Variance tradeoff/[FTU Forum].url 1.4 kB32.5 - Train and run time complexity/[FTU Forum].url 1.4 kB32.6 - BaggingCode Sample/[FTU Forum].url 1.4 kB32.7 - Extremely randomized trees/[FTU Forum].url 1.4 kB32.8 - Random Tree Cases/[FTU Forum].url 1.4 kB32.9 - Boosting Intuition/[FTU Forum].url 1.4 kB33.1 - Introduction/[FTU Forum].url 1.4 kB33.10 - Indicator variables/[FTU Forum].url 1.4 kB33.11 - Feature binning/[FTU Forum].url 1.4 kB33.12 - Interaction variables/[FTU Forum].url 1.4 kB33.13 - Mathematical transforms/[FTU Forum].url 1.4 kB33.14 - Model specific featurizations/[FTU Forum].url 1.4 kB33.15 - Feature orthogonality/[FTU Forum].url 1.4 kB33.16 - Domain specific featurizations/[FTU Forum].url 1.4 kB33.17 - Feature slicing/[FTU Forum].url 1.4 kB33.18 - Kaggle Winners solutions/[FTU Forum].url 1.4 kB33.2 - Moving window for Time Series Data/[FTU Forum].url 1.4 kB33.3 - Fourier decomposition/[FTU Forum].url 1.4 kB33.4 - Deep learning features LSTM/[FTU Forum].url 1.4 kB33.5 - Image histogram/[FTU Forum].url 1.4 kB33.6 - Keypoints SIFT/[FTU Forum].url 1.4 kB33.7 - Deep learning features CNN/[FTU Forum].url 1.4 kB33.8 - Relational data/[FTU Forum].url 1.4 kB33.9 - Graph data/[FTU Forum].url 1.4 kB34.1 - Calibration of ModelsNeed for calibration/[FTU Forum].url 1.4 kB34.10 - AB testing/[FTU Forum].url 1.4 kB34.11 - Data Science Life cycle/[FTU Forum].url 1.4 kB34.12 - VC dimension/[FTU Forum].url 1.4 kB34.2 - Productionization and deployment of Machine Learning Models/[FTU Forum].url 1.4 kB34.3 - Calibration Plots/[FTU Forum].url 1.4 kB34.4 - Platt’s CalibrationScaling/[FTU Forum].url 1.4 kB34.5 - Isotonic Regression/[FTU Forum].url 1.4 kB34.6 - Code Samples/[FTU Forum].url 1.4 kB34.7 - Modeling in the presence of outliers RANSAC/[FTU Forum].url 1.4 kB34.8 - Productionizing models/[FTU Forum].url 1.4 kB34.9 - Retraining models periodically/[FTU Forum].url 1.4 kB35.1 - What is Clustering/[FTU Forum].url 1.4 kB35.10 - K-Medoids/[FTU Forum].url 1.4 kB35.11 - Determining the right K/[FTU Forum].url 1.4 kB35.12 - Code Samples/[FTU Forum].url 1.4 kB35.13 - Time and space complexity/[FTU Forum].url 1.4 kB35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FTU Forum].url 1.4 kB35.2 - Unsupervised learning/[FTU Forum].url 1.4 kB35.3 - Applications/[FTU Forum].url 1.4 kB35.4 - Metrics for Clustering/[FTU Forum].url 1.4 kB35.5 - K-Means Geometric intuition, Centroids/[FTU Forum].url 1.4 kB35.6 - K-Means Mathematical formulation Objective function/[FTU Forum].url 1.4 kB35.7 - K-Means Algorithm/[FTU Forum].url 1.4 kB35.8 - How to initialize K-Means++/[FTU Forum].url 1.4 kB35.9 - Failure casesLimitations/[FTU Forum].url 1.4 kB36.1 - Agglomerative & Divisive, Dendrograms/[FTU Forum].url 1.4 kB36.2 - Agglomerative Clustering/[FTU Forum].url 1.4 kB36.3 - Proximity methods Advantages and Limitations/[FTU Forum].url 1.4 kB36.4 - Time and Space Complexity/[FTU Forum].url 1.4 kB36.5 - Limitations of Hierarchical Clustering/[FTU Forum].url 1.4 kB36.6 - Code sample/[FTU Forum].url 1.4 kB36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FTU Forum].url 1.4 kB37.1 - Density based clustering/[FTU Forum].url 1.4 kB37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FTU Forum].url 1.4 kB37.11 - Revision Questions/[FTU Forum].url 1.4 kB37.2 - MinPts and Eps Density/[FTU Forum].url 1.4 kB37.3 - Core, Border and Noise points/[FTU Forum].url 1.4 kB37.4 - Density edge and Density connected points/[FTU Forum].url 1.4 kB37.5 - DBSCAN Algorithm/[FTU Forum].url 1.4 kB37.6 - Hyper Parameters MinPts and Eps/[FTU Forum].url 1.4 kB37.7 - Advantages and Limitations of DBSCAN/[FTU Forum].url 1.4 kB37.8 - Time and Space Complexity/[FTU Forum].url 1.4 kB37.9 - Code samples/[FTU Forum].url 1.4 kB38.1 - Problem formulation Movie reviews/[FTU Forum].url 1.4 kB38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/[FTU Forum].url 1.4 kB38.11 - Cold Start problem/[FTU Forum].url 1.4 kB38.12 - Word vectors as MF/[FTU Forum].url 1.4 kB38.13 - Eigen-Faces/[FTU Forum].url 1.4 kB38.14 - Code example/[FTU Forum].url 1.4 kB38.15 - Assignment-11 Apply Truncated SVD/[FTU Forum].url 1.4 kB38.16 - Revision Questions/[FTU Forum].url 1.4 kB38.2 - Content based vs Collaborative Filtering/[FTU Forum].url 1.4 kB38.3 - Similarity based Algorithms/[FTU Forum].url 1.4 kB38.4 - Matrix Factorization PCA, SVD/[FTU Forum].url 1.4 kB38.5 - Matrix Factorization NMF/[FTU Forum].url 1.4 kB38.6 - Matrix Factorization for Collaborative filtering/[FTU Forum].url 1.4 kB38.7 - Matrix Factorization for feature engineering/[FTU Forum].url 1.4 kB38.8 - Clustering as MF/[FTU Forum].url 1.4 kB38.9 - Hyperparameter tuning/[FTU Forum].url 1.4 kB39.1 - Questions & Answers/[FTU Forum].url 1.4 kB4.1 - Introduction/[FTU Forum].url 1.4 kB4.10 - Debugging Python/[FTU Forum].url 1.4 kB4.2 - Types of functions/[FTU Forum].url 1.4 kB4.3 - Function arguments/[FTU Forum].url 1.4 kB4.4 - Recursive functions/[FTU Forum].url 1.4 kB4.5 - Lambda functions/[FTU Forum].url 1.4 kB4.6 - Modules/[FTU Forum].url 1.4 kB4.7 - Packages/[FTU Forum].url 1.4 kB4.8 - File Handling/[FTU Forum].url 1.4 kB4.9 - Exception Handling/[FTU Forum].url 1.4 kB40.1 - BusinessReal world problem/[FTU Forum].url 1.4 kB40.10 - Data Modeling Multi label Classification/[FTU Forum].url 1.4 kB40.11 - Data preparation/[FTU Forum].url 1.4 kB40.12 - Train-Test Split/[FTU Forum].url 1.4 kB40.13 - Featurization/[FTU Forum].url 1.4 kB40.14 - Logistic regression One VS Rest/[FTU Forum].url 1.4 kB40.15 - Sampling data and tags+Weighted models/[FTU Forum].url 1.4 kB40.16 - Logistic regression revisited/[FTU Forum].url 1.4 kB40.17 - Why not use advanced techniques/[FTU Forum].url 1.4 kB40.18 - Assignments/[FTU Forum].url 1.4 kB40.2 - Business objectives and constraints/[FTU Forum].url 1.4 kB40.3 - Mapping to an ML problem Data overview/[FTU Forum].url 1.4 kB40.4 - Mapping to an ML problemML problem formulation/[FTU Forum].url 1.4 kB40.5 - Mapping to an ML problemPerformance metrics/[FTU Forum].url 1.4 kB40.6 - Hamming loss/[FTU Forum].url 1.4 kB40.7 - EDAData Loading/[FTU Forum].url 1.4 kB40.8 - EDAAnalysis of tags/[FTU Forum].url 1.4 kB40.9 - EDAData Preprocessing/[FTU Forum].url 1.4 kB41.1 - BusinessReal world problem Problem definition/[FTU Forum].url 1.4 kB41.10 - EDA Feature analysis/[FTU Forum].url 1.4 kB41.11 - EDA Data Visualization T-SNE/[FTU Forum].url 1.4 kB41.12 - EDA TF-IDF weighted Word2Vec featurization/[FTU Forum].url 1.4 kB41.13 - ML Models Loading Data/[FTU Forum].url 1.4 kB41.14 - ML Models Random Model/[FTU Forum].url 1.4 kB41.15 - ML Models Logistic Regression and Linear SVM/[FTU Forum].url 1.4 kB41.16 - ML Models XGBoost/[FTU Forum].url 1.4 kB41.17 - Assignments/[FTU Forum].url 1.4 kB41.2 - Business objectives and constraints/[FTU Forum].url 1.4 kB41.3 - Mapping to an ML problem Data overview/[FTU Forum].url 1.4 kB41.4 - Mapping to an ML problem ML problem and performance metric/[FTU Forum].url 1.4 kB41.5 - Mapping to an ML problem Train-test split/[FTU Forum].url 1.4 kB41.6 - EDA Basic Statistics/[FTU Forum].url 1.4 kB41.7 - EDA Basic Feature Extraction/[FTU Forum].url 1.4 kB41.8 - EDA Text Preprocessing/[FTU Forum].url 1.4 kB41.9 - EDA Advanced Feature Extraction/[FTU Forum].url 1.4 kB42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/[FTU Forum].url 1.4 kB42.10 - Text Pre-Processing Tokenization and Stop-word removal/[FTU Forum].url 1.4 kB42.11 - Stemming/[FTU Forum].url 1.4 kB42.12 - Text based product similarity Converting text to an n-D vector bag of words/[FTU Forum].url 1.4 kB42.13 - Code for bag of words based product similarity/[FTU Forum].url 1.4 kB42.14 - TF-IDF featurizing text based on word-importance/[FTU Forum].url 1.4 kB42.15 - Code for TF-IDF based product similarity/[FTU Forum].url 1.4 kB42.16 - Code for IDF based product similarity/[FTU Forum].url 1.4 kB42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/[FTU Forum].url 1.4 kB42.18 - Code for Average Word2Vec product similarity/[FTU Forum].url 1.4 kB42.19 - TF-IDF weighted Word2Vec/[FTU Forum].url 1.4 kB42.2 - Plan of action/[FTU Forum].url 1.4 kB42.20 - Code for IDF weighted Word2Vec product similarity/[FTU Forum].url 1.4 kB42.21 - Weighted similarity using brand and color/[FTU Forum].url 1.4 kB42.22 - Code for weighted similarity/[FTU Forum].url 1.4 kB42.23 - Building a real world solution/[FTU Forum].url 1.4 kB42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/[FTU Forum].url 1.4 kB42.25 - Using Keras + Tensorflow to extract features/[FTU Forum].url 1.4 kB42.26 - Visual similarity based product similarity/[FTU Forum].url 1.4 kB42.27 - Measuring goodness of our solution AB testing/[FTU Forum].url 1.4 kB42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/[FTU Forum].url 1.4 kB42.3 - Amazon product advertising API/[FTU Forum].url 1.4 kB42.4 - Data folders and paths/[FTU Forum].url 1.4 kB42.5 - Overview of the data and Terminology/[FTU Forum].url 1.4 kB42.6 - Data cleaning and understandingMissing data in various features/[FTU Forum].url 1.4 kB42.7 - Understand duplicate rows/[FTU Forum].url 1.4 kB42.8 - Remove duplicates Part 1/[FTU Forum].url 1.4 kB42.9 - Remove duplicates Part 2/[FTU Forum].url 1.4 kB43.1 - Businessreal world problem Problem definition/[FTU Forum].url 1.4 kB43.10 - ML models – using byte files only Random Model/[FTU Forum].url 1.4 kB43.11 - k-NN/[FTU Forum].url 1.4 kB43.12 - Logistic regression/[FTU Forum].url 1.4 kB43.13 - Random Forest and Xgboost/[FTU Forum].url 1.4 kB43.14 - ASM Files Feature extraction & Multiprocessing/[FTU Forum].url 1.4 kB43.15 - File-size feature/[FTU Forum].url 1.4 kB43.16 - Univariate analysis/[FTU Forum].url 1.4 kB43.17 - t-SNE analysis/[FTU Forum].url 1.4 kB43.18 - ML models on ASM file features/[FTU Forum].url 1.4 kB43.19 - Models on all features t-SNE/[FTU Forum].url 1.4 kB43.2 - Businessreal world problem Objectives and constraints/[FTU Forum].url 1.4 kB43.20 - Models on all features RandomForest and Xgboost/[FTU Forum].url 1.4 kB43.21 - Assignments/[FTU Forum].url 1.4 kB43.3 - Machine Learning problem mapping Data overview/[FTU Forum].url 1.4 kB43.4 - Machine Learning problem mapping ML problem/[FTU Forum].url 1.4 kB43.5 - Machine Learning problem mapping Train and test splitting/[FTU Forum].url 1.4 kB43.6 - Exploratory Data Analysis Class distribution/[FTU Forum].url 1.4 kB43.7 - Exploratory Data Analysis Feature extraction from byte files/[FTU Forum].url 1.4 kB43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/[FTU Forum].url 1.4 kB43.9 - Exploratory Data Analysis Train-Test class distribution/[FTU Forum].url 1.4 kB44.1 - BusinessReal world problemProblem definition/[FTU Forum].url 1.4 kB44.10 - Exploratory Data AnalysisCold start problem/[FTU Forum].url 1.4 kB44.11 - Computing Similarity matricesUser-User similarity matrix/[FTU Forum].url 1.4 kB44.12 - Computing Similarity matricesMovie-Movie similarity/[FTU Forum].url 1.4 kB44.13 - Computing Similarity matricesDoes movie-movie similarity work/[FTU Forum].url 1.4 kB44.14 - ML ModelsSurprise library/[FTU Forum].url 1.4 kB44.15 - Overview of the modelling strategy/[FTU Forum].url 1.4 kB44.16 - Data Sampling/[FTU Forum].url 1.4 kB44.17 - Google drive with intermediate files/[FTU Forum].url 1.4 kB44.18 - Featurizations for regression/[FTU Forum].url 1.4 kB44.19 - Data transformation for Surprise/[FTU Forum].url 1.4 kB44.2 - Objectives and constraints/[FTU Forum].url 1.4 kB44.20 - Xgboost with 13 features/[FTU Forum].url 1.4 kB44.21 - Surprise Baseline model/[FTU Forum].url 1.4 kB44.22 - Xgboost + 13 features +Surprise baseline model/[FTU Forum].url 1.4 kB44.23 - Surprise KNN predictors/[FTU Forum].url 1.4 kB44.24 - Matrix Factorization models using Surprise/[FTU Forum].url 1.4 kB44.25 - SVD ++ with implicit feedback/[FTU Forum].url 1.4 kB44.26 - Final models with all features and predictors/[FTU Forum].url 1.4 kB44.27 - Comparison between various models/[FTU Forum].url 1.4 kB44.28 - Assignments/[FTU Forum].url 1.4 kB44.3 - Mapping to an ML problemData overview/[FTU Forum].url 1.4 kB44.4 - Mapping to an ML problemML problem formulation/[FTU Forum].url 1.4 kB44.5 - Exploratory Data AnalysisData preprocessing/[FTU Forum].url 1.4 kB44.6 - Exploratory Data AnalysisTemporal Train-Test split/[FTU Forum].url 1.4 kB44.7 - Exploratory Data AnalysisPreliminary data analysis/[FTU Forum].url 1.4 kB44.8 - Exploratory Data AnalysisSparse matrix representation/[FTU Forum].url 1.4 kB44.9 - Exploratory Data AnalysisAverage ratings for various slices/[FTU Forum].url 1.4 kB45.1 - BusinessReal world problem Overview/[FTU Forum].url 1.4 kB45.10 - Univariate AnalysisVariation Feature/[FTU Forum].url 1.4 kB45.11 - Univariate AnalysisText feature/[FTU Forum].url 1.4 kB45.12 - Machine Learning ModelsData preparation/[FTU Forum].url 1.4 kB45.13 - Baseline Model Naive Bayes/[FTU Forum].url 1.4 kB45.14 - K-Nearest Neighbors Classification/[FTU Forum].url 1.4 kB45.15 - Logistic Regression with class balancing/[FTU Forum].url 1.4 kB45.16 - Logistic Regression without class balancing/[FTU Forum].url 1.4 kB45.17 - Linear-SVM/[FTU Forum].url 1.4 kB45.18 - Random-Forest with one-hot encoded features/[FTU Forum].url 1.4 kB45.19 - Random-Forest with response-coded features/[FTU Forum].url 1.4 kB45.2 - Business objectives and constraints/[FTU Forum].url 1.4 kB45.20 - Stacking Classifier/[FTU Forum].url 1.4 kB45.21 - Majority Voting classifier/[FTU Forum].url 1.4 kB45.22 - Assignments/[FTU Forum].url 1.4 kB45.3 - ML problem formulation Data/[FTU Forum].url 1.4 kB45.4 - ML problem formulation Mapping real world to ML problem#/[FTU Forum].url 1.4 kB45.4 - ML problem formulation Mapping real world to ML problem/[FTU Forum].url 1.4 kB45.5 - ML problem formulation Train, CV and Test data construction/[FTU Forum].url 1.4 kB45.6 - Exploratory Data AnalysisReading data & preprocessing/[FTU Forum].url 1.4 kB45.7 - Exploratory Data AnalysisDistribution of Class-labels/[FTU Forum].url 1.4 kB45.8 - Exploratory Data Analysis “Random” Model/[FTU Forum].url 1.4 kB45.9 - Univariate AnalysisGene feature/[FTU Forum].url 1.4 kB46.1 - BusinessReal world problem Overview/[FTU Forum].url 1.4 kB46.10 - Data Cleaning Speed/[FTU Forum].url 1.4 kB46.11 - Data Cleaning Distance/[FTU Forum].url 1.4 kB46.12 - Data Cleaning Fare/[FTU Forum].url 1.4 kB46.13 - Data Cleaning Remove all outlierserroneous points/[FTU Forum].url 1.4 kB46.14 - Data PreparationClusteringSegmentation/[FTU Forum].url 1.4 kB46.15 - Data PreparationTime binning/[FTU Forum].url 1.4 kB46.16 - Data PreparationSmoothing time-series data/[FTU Forum].url 1.4 kB46.17 - Data PreparationSmoothing time-series data cont/[FTU Forum].url 1.4 kB46.18 - Data Preparation Time series and Fourier transforms/[FTU Forum].url 1.4 kB46.19 - Ratios and previous-time-bin values/[FTU Forum].url 1.4 kB46.2 - Objectives and Constraints/[FTU Forum].url 1.4 kB46.20 - Simple moving average/[FTU Forum].url 1.4 kB46.21 - Weighted Moving average/[FTU Forum].url 1.4 kB46.22 - Exponential weighted moving average/[FTU Forum].url 1.4 kB46.23 - Results/[FTU Forum].url 1.4 kB46.24 - Regression models Train-Test split & Features/[FTU Forum].url 1.4 kB46.25 - Linear regression/[FTU Forum].url 1.4 kB46.26 - Random Forest regression/[FTU Forum].url 1.4 kB46.27 - Xgboost Regression/[FTU Forum].url 1.4 kB46.28 - Model comparison/[FTU Forum].url 1.4 kB46.29 - Assignment/[FTU Forum].url 1.4 kB46.3 - Mapping to ML problem Data/[FTU Forum].url 1.4 kB46.4 - Mapping to ML problem dask dataframes/[FTU Forum].url 1.4 kB46.5 - Mapping to ML problem FieldsFeatures/[FTU Forum].url 1.4 kB46.6 - Mapping to ML problem Time series forecastingRegression/[FTU Forum].url 1.4 kB46.7 - Mapping to ML problem Performance metrics/[FTU Forum].url 1.4 kB46.8 - Data Cleaning Latitude and Longitude data/[FTU Forum].url 1.4 kB46.9 - Data Cleaning Trip Duration/[FTU Forum].url 1.4 kB47.1 - History of Neural networks and Deep Learning/[FTU Forum].url 1.4 kB47.10 - Backpropagation/[FTU Forum].url 1.4 kB47.11 - Activation functions/[FTU Forum].url 1.4 kB47.12 - Vanishing Gradient problem/[FTU Forum].url 1.4 kB47.13 - Bias-Variance tradeoff/[FTU Forum].url 1.4 kB47.14 - Decision surfaces Playground/[FTU Forum].url 1.4 kB47.2 - How Biological Neurons work/[FTU Forum].url 1.4 kB47.3 - Growth of biological neural networks/[FTU Forum].url 1.4 kB47.4 - Diagrammatic representation Logistic Regression and Perceptron/[FTU Forum].url 1.4 kB47.5 - Multi-Layered Perceptron (MLP)/[FTU Forum].url 1.4 kB47.6 - Notation/[FTU Forum].url 1.4 kB47.7 - Training a single-neuron model/[FTU Forum].url 1.4 kB47.8 - Training an MLP Chain Rule/[FTU Forum].url 1.4 kB47.9 - Training an MLPMemoization/[FTU Forum].url 1.4 kB48.1 - Deep Multi-layer perceptrons1980s to 2010s/[FTU Forum].url 1.4 kB48.10 - Nesterov Accelerated Gradient (NAG)/[FTU Forum].url 1.4 kB48.11 - OptimizersAdaGrad/[FTU Forum].url 1.4 kB48.12 - Optimizers Adadelta andRMSProp/[FTU Forum].url 1.4 kB48.13 - Adam/[FTU Forum].url 1.4 kB48.14 - Which algorithm to choose when/[FTU Forum].url 1.4 kB48.15 - Gradient Checking and clipping/[FTU Forum].url 1.4 kB48.16 - Softmax and Cross-entropy for multi-class classification/[FTU Forum].url 1.4 kB48.17 - How to train a Deep MLP/[FTU Forum].url 1.4 kB48.18 - Auto Encoders/[FTU Forum].url 1.4 kB48.19 - Word2Vec CBOW/[FTU Forum].url 1.4 kB48.2 - Dropout layers & Regularization/[FTU Forum].url 1.4 kB48.20 - Word2Vec Skip-gram/[FTU Forum].url 1.4 kB48.21 - Word2Vec Algorithmic Optimizations/[FTU Forum].url 1.4 kB48.3 - Rectified Linear Units (ReLU)/[FTU Forum].url 1.4 kB48.4 - Weight initialization/[FTU Forum].url 1.4 kB48.5 - Batch Normalization/[FTU Forum].url 1.4 kB48.6 - OptimizersHill-descent analogy in 2D/[FTU Forum].url 1.4 kB48.7 - OptimizersHill descent in 3D and contours/[FTU Forum].url 1.4 kB48.8 - SGD Recap/[FTU Forum].url 1.4 kB48.9 - Batch SGD with momentum/[FTU Forum].url 1.4 kB49.1 - Tensorflow and Keras overview/[FTU Forum].url 1.4 kB49.10 - Model 3 Batch Normalization/[FTU Forum].url 1.4 kB49.11 - Model 4 Dropout/[FTU Forum].url 1.4 kB49.12 - MNIST classification in Keras/[FTU Forum].url 1.4 kB49.13 - Hyperparameter tuning in Keras/[FTU Forum].url 1.4 kB49.14 - Exercise Try different MLP architectures on MNIST dataset/[FTU Forum].url 1.4 kB49.2 - GPU vs CPU for Deep Learning/[FTU Forum].url 1.4 kB49.3 - Google Colaboratory/[FTU Forum].url 1.4 kB49.4 - Install TensorFlow/[FTU Forum].url 1.4 kB49.5 - Online documentation and tutorials/[FTU Forum].url 1.4 kB49.6 - Softmax Classifier on MNIST dataset/[FTU Forum].url 1.4 kB49.7 - MLP Initialization/[FTU Forum].url 1.4 kB49.8 - Model 1 Sigmoid activation/[FTU Forum].url 1.4 kB49.9 - Model 2 ReLU activation/[FTU Forum].url 1.4 kB5.1 - Numpy Introduction/[FTU Forum].url 1.4 kB5.2 - Numerical operations on Numpy/[FTU Forum].url 1.4 kB50.1 - Biological inspiration Visual Cortex/[FTU Forum].url 1.4 kB50.10 - Data Augmentation/[FTU Forum].url 1.4 kB50.11 - Convolution Layers in Keras/[FTU Forum].url 1.4 kB50.12 - AlexNet/[FTU Forum].url 1.4 kB50.13 - VGGNet/[FTU Forum].url 1.4 kB50.14 - Residual Network/[FTU Forum].url 1.4 kB50.15 - Inception Network/[FTU Forum].url 1.4 kB50.16 - What is Transfer learning/[FTU Forum].url 1.4 kB50.17 - Code example Cats vs Dogs/[FTU Forum].url 1.4 kB50.18 - Code Example MNIST dataset/[FTU Forum].url 1.4 kB50.19 - Assignment Try various CNN networks on MNIST dataset#/[FTU Forum].url 1.4 kB50.2 - ConvolutionEdge Detection on images/[FTU Forum].url 1.4 kB50.3 - ConvolutionPadding and strides/[FTU Forum].url 1.4 kB50.4 - Convolution over RGB images/[FTU Forum].url 1.4 kB50.5 - Convolutional layer/[FTU Forum].url 1.4 kB50.6 - Max-pooling/[FTU Forum].url 1.4 kB50.7 - CNN Training Optimization/[FTU Forum].url 1.4 kB50.8 - Example CNN LeNet [1998]/[FTU Forum].url 1.4 kB50.9 - ImageNet dataset/[FTU Forum].url 1.4 kB51.1 - Why RNNs/[FTU Forum].url 1.4 kB51.10 - Code example IMDB Sentiment classification/[FTU Forum].url 1.4 kB51.11 - Exercise Amazon Fine Food reviews LSTM model/[FTU Forum].url 1.4 kB51.2 - Recurrent Neural Network/[FTU Forum].url 1.4 kB51.3 - Training RNNs Backprop/[FTU Forum].url 1.4 kB51.4 - Types of RNNs/[FTU Forum].url 1.4 kB51.5 - Need for LSTMGRU/[FTU Forum].url 1.4 kB51.6 - LSTM/[FTU Forum].url 1.4 kB51.7 - GRUs/[FTU Forum].url 1.4 kB51.8 - Deep RNN/[FTU Forum].url 1.4 kB51.9 - Bidirectional RNN/[FTU Forum].url 1.4 kB52.1 - Questions and Answers/[FTU Forum].url 1.4 kB53.1 - Self Driving Car Problem definition/[FTU Forum].url 1.4 kB53.10 - NVIDIA’s end to end CNN model/[FTU Forum].url 1.4 kB53.11 - Train the model/[FTU Forum].url 1.4 kB53.12 - Test and visualize the output/[FTU Forum].url 1.4 kB53.13 - Extensions/[FTU Forum].url 1.4 kB53.14 - Assignment/[FTU Forum].url 1.4 kB53.2 - Datasets#/[FTU Forum].url 1.4 kB53.2 - Datasets/[FTU Forum].url 1.4 kB53.3 - Data understanding & Analysis Files and folders/[FTU Forum].url 1.4 kB53.4 - Dash-cam images and steering angles/[FTU Forum].url 1.4 kB53.5 - Split the dataset Train vs Test/[FTU Forum].url 1.4 kB53.6 - EDA Steering angles/[FTU Forum].url 1.4 kB53.7 - Mean Baseline model simple/[FTU Forum].url 1.4 kB53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/[FTU Forum].url 1.4 kB53.9 - Batch load the dataset/[FTU Forum].url 1.4 kB54.1 - Real-world problem/[FTU Forum].url 1.4 kB54.10 - MIDI music generation/[FTU Forum].url 1.4 kB54.11 - Survey blog/[FTU Forum].url 1.4 kB54.2 - Music representation/[FTU Forum].url 1.4 kB54.3 - Char-RNN with abc-notation Char-RNN model/[FTU Forum].url 1.4 kB54.4 - Char-RNN with abc-notation Data preparation/[FTU Forum].url 1.4 kB54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/[FTU Forum].url 1.4 kB54.6 - Char-RNN with abc-notation State full RNN/[FTU Forum].url 1.4 kB54.7 - Char-RNN with abc-notation Model architecture,Model training/[FTU Forum].url 1.4 kB54.8 - Char-RNN with abc-notation Music generation/[FTU Forum].url 1.4 kB54.9 - Char-RNN with abc-notation Generate tabla music/[FTU Forum].url 1.4 kB55.1 - Human Activity Recognition Problem definition/[FTU Forum].url 1.4 kB55.2 - Dataset understanding/[FTU Forum].url 1.4 kB55.3 - Data cleaning & preprocessing/[FTU Forum].url 1.4 kB55.4 - EDAUnivariate analysis/[FTU Forum].url 1.4 kB55.5 - EDAData visualization using t-SNE/[FTU Forum].url 1.4 kB55.6 - Classical ML models/[FTU Forum].url 1.4 kB55.7 - Deep-learning Model/[FTU Forum].url 1.4 kB55.8 - Exercise Build deeper LSTM models and hyper-param tune them/[FTU Forum].url 1.4 kB56.1 - Problem definition/[FTU Forum].url 1.4 kB56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/[FTU Forum].url 1.4 kB56.11 - PageRank/[FTU Forum].url 1.4 kB56.12 - Shortest Path/[FTU Forum].url 1.4 kB56.13 - Connected-components/[FTU Forum].url 1.4 kB56.14 - Adar Index/[FTU Forum].url 1.4 kB56.15 - Kartz Centrality/[FTU Forum].url 1.4 kB56.16 - HITS Score/[FTU Forum].url 1.4 kB56.17 - SVD/[FTU Forum].url 1.4 kB56.18 - Weight features/[FTU Forum].url 1.4 kB56.19 - Modeling/[FTU Forum].url 1.4 kB56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/[FTU Forum].url 1.4 kB56.3 - Data format & Limitations/[FTU Forum].url 1.4 kB56.4 - Mapping to a supervised classification problem/[FTU Forum].url 1.4 kB56.5 - Business constraints & Metrics/[FTU Forum].url 1.4 kB56.6 - EDABasic Stats/[FTU Forum].url 1.4 kB56.7 - EDAFollower and following stats/[FTU Forum].url 1.4 kB56.8 - EDABinary Classification Task/[FTU Forum].url 1.4 kB56.9 - EDATrain and test split/[FTU Forum].url 1.4 kB57.1 - Introduction to Databases/[FTU Forum].url 1.4 kB57.10 - ORDER BY/[FTU Forum].url 1.4 kB57.11 - DISTINCT/[FTU Forum].url 1.4 kB57.12 - WHERE, Comparison operators, NULL/[FTU Forum].url 1.4 kB57.13 - Logical Operators/[FTU Forum].url 1.4 kB57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/[FTU Forum].url 1.4 kB57.15 - GROUP BY/[FTU Forum].url 1.4 kB57.16 - HAVING/[FTU Forum].url 1.4 kB57.17 - Order of keywords#/[FTU Forum].url 1.4 kB57.18 - Join and Natural Join/[FTU Forum].url 1.4 kB57.19 - Inner, Left, Right and Outer joins/[FTU Forum].url 1.4 kB57.2 - Why SQL/[FTU Forum].url 1.4 kB57.20 - Sub QueriesNested QueriesInner Queries/[FTU Forum].url 1.4 kB57.21 - DMLINSERT/[FTU Forum].url 1.4 kB57.22 - DMLUPDATE , DELETE/[FTU Forum].url 1.4 kB57.23 - DDLCREATE TABLE/[FTU Forum].url 1.4 kB57.24 - DDLALTER ADD, MODIFY, DROP/[FTU Forum].url 1.4 kB57.25 - DDLDROP TABLE, TRUNCATE, DELETE/[FTU Forum].url 1.4 kB57.26 - Data Control Language GRANT, REVOKE/[FTU Forum].url 1.4 kB57.27 - Learning resources/[FTU Forum].url 1.4 kB57.3 - Execution of an SQL statement/[FTU Forum].url 1.4 kB57.4 - IMDB dataset/[FTU Forum].url 1.4 kB57.5 - Installing MySQL/[FTU Forum].url 1.4 kB57.6 - Load IMDB data/[FTU Forum].url 1.4 kB57.7 - USE, DESCRIBE, SHOW TABLES/[FTU Forum].url 1.4 kB57.8 - SELECT/[FTU Forum].url 1.4 kB57.9 - LIMIT, OFFSET/[FTU Forum].url 1.4 kB58.1 - AD-Click Predicition/[FTU Forum].url 1.4 kB59.1 - Revision Questions/[FTU Forum].url 1.4 kB59.2 - Questions/[FTU Forum].url 1.4 kB59.3 - External resources for Interview Questions/[FTU Forum].url 1.4 kB6.1 - Getting started with Matplotlib/[FTU Forum].url 1.4 kB7.1 - Getting started with pandas/[FTU Forum].url 1.4 kB7.2 - Data Frame Basics/[FTU Forum].url 1.4 kB7.3 - Key Operations on Data Frames/[FTU Forum].url 1.4 kB8.1 - Space and Time Complexity Find largest number in a list/[FTU Forum].url 1.4 kB8.2 - Binary search/[FTU Forum].url 1.4 kB8.3 - Find elements common in two lists/[FTU Forum].url 1.4 kB8.4 - Find elements common in two lists using a HashtableDict/[FTU Forum].url 1.4 kB9.1 - Introduction to IRIS dataset and 2D scatter plot/[FTU Forum].url 1.4 kB9.10 - Percentiles and Quantiles/[FTU Forum].url 1.4 kB9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/[FTU Forum].url 1.4 kB9.12 - Box-plot with Whiskers/[FTU Forum].url 1.4 kB9.13 - Violin Plots/[FTU Forum].url 1.4 kB9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/[FTU Forum].url 1.4 kB9.15 - Multivariate Probability Density, Contour Plot/[FTU Forum].url 1.4 kB9.16 - Exercise Perform EDA on Haberman dataset/[FTU Forum].url 1.4 kB9.2 - 3D scatter plot/[FTU Forum].url 1.4 kB9.3 - Pair plots/[FTU Forum].url 1.4 kB9.4 - Limitations of Pair Plots/[FTU Forum].url 1.4 kB9.5 - Histogram and Introduction to PDF(Probability Density Function)/[FTU Forum].url 1.4 kB9.6 - Univariate Analysis using PDF/[FTU Forum].url 1.4 kB9.7 - CDF(Cumulative Distribution Function)/[FTU Forum].url 1.4 kB9.8 - Mean, Variance and Standard Deviation/[FTU Forum].url 1.4 kB9.9 - Median/[FTU Forum].url 1.4 kB58.1 - AD-Click Predicition/out_files/iframe_api 859 Bytes58.1 - AD-Click Predicition/out_files/api.js.download 796 Bytes1.1 - How to Learn from Appliedaicourse/How you can help Team-FTU.txt 241 Bytes1.2 - How the Job Guarantee program works/How you can help Team-FTU.txt 241 Bytes10.1 - Why learn it/How you can help Team-FTU.txt 241 Bytes10.10 - Hyper Cube,Hyper Cuboid/How you can help Team-FTU.txt 241 Bytes10.11 - Revision Questions/How you can help Team-FTU.txt 241 Bytes10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/How you can help Team-FTU.txt 241 Bytes10.3 - Dot Product and Angle between 2 Vectors/How you can help Team-FTU.txt 241 Bytes10.4 - Projection and Unit Vector/How you can help Team-FTU.txt 241 Bytes10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/How you can help Team-FTU.txt 241 Bytes10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/How you can help Team-FTU.txt 241 Bytes10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/How you can help Team-FTU.txt 241 Bytes10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/How you can help Team-FTU.txt 241 Bytes10.9 - Square ,Rectangle/How you can help Team-FTU.txt 241 Bytes11.1 - Introduction to Probability and Statistics/How you can help Team-FTU.txt 241 Bytes11.10 - How distributions are used/How you can help Team-FTU.txt 241 Bytes11.11 - Chebyshev’s inequality/How you can help Team-FTU.txt 241 Bytes11.12 - Discrete and Continuous Uniform distributions/How you can help Team-FTU.txt 241 Bytes11.13 - How to randomly sample data points (Uniform Distribution)/How you can help Team-FTU.txt 241 Bytes11.14 - Bernoulli and Binomial Distribution/How you can help Team-FTU.txt 241 Bytes11.15 - Log Normal Distribution/How you can help Team-FTU.txt 241 Bytes11.16 - Power law distribution/How you can help Team-FTU.txt 241 Bytes11.17 - Box cox transform/How you can help Team-FTU.txt 241 Bytes11.18 - Applications of non-gaussian distributions/How you can help Team-FTU.txt 241 Bytes11.19 - Co-variance/How you can help Team-FTU.txt 241 Bytes11.2 - Population and Sample/How you can help Team-FTU.txt 241 Bytes11.20 - Pearson Correlation Coefficient/How you can help Team-FTU.txt 241 Bytes11.21 - Spearman Rank Correlation Coefficient/How you can help Team-FTU.txt 241 Bytes11.22 - Correlation vs Causation/How you can help Team-FTU.txt 241 Bytes11.23 - How to use correlations/How you can help Team-FTU.txt 241 Bytes11.24 - Confidence interval (C.I) Introduction/How you can help Team-FTU.txt 241 Bytes11.25 - Computing confidence interval given the underlying distribution/How you can help Team-FTU.txt 241 Bytes11.26 - C.I for mean of a normal random variable/How you can help Team-FTU.txt 241 Bytes11.27 - Confidence interval using bootstrapping/How you can help Team-FTU.txt 241 Bytes11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/How you can help Team-FTU.txt 241 Bytes11.29 - Hypothesis Testing Intution with coin toss example/How you can help Team-FTU.txt 241 Bytes11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/How you can help Team-FTU.txt 241 Bytes11.30 - Resampling and permutation test/How you can help Team-FTU.txt 241 Bytes11.31 - K-S Test for similarity of two distributions/How you can help Team-FTU.txt 241 Bytes11.32 - Code Snippet K-S Test/How you can help Team-FTU.txt 241 Bytes11.33 - Hypothesis testing another example/How you can help Team-FTU.txt 241 Bytes11.34 - Resampling and Permutation test another example/How you can help Team-FTU.txt 241 Bytes11.35 - How to use hypothesis testing/How you can help Team-FTU.txt 241 Bytes11.36 - Proportional Sampling/How you can help Team-FTU.txt 241 Bytes11.37 - Revision Questions/How you can help Team-FTU.txt 241 Bytes11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/How you can help Team-FTU.txt 241 Bytes11.5 - Symmetric distribution, Skewness and Kurtosis/How you can help Team-FTU.txt 241 Bytes11.6 - Standard normal variate (Z) and standardization/How you can help Team-FTU.txt 241 Bytes11.7 - Kernel density estimation/How you can help Team-FTU.txt 241 Bytes11.8 - Sampling distribution & Central Limit theorem/How you can help Team-FTU.txt 241 Bytes11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/How you can help Team-FTU.txt 241 Bytes12.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes13.1 - What is Dimensionality reduction/How you can help Team-FTU.txt 241 Bytes13.10 - Code to Load MNIST Data Set/How you can help Team-FTU.txt 241 Bytes13.2 - Row Vector and Column Vector/How you can help Team-FTU.txt 241 Bytes13.3 - How to represent a data set/How you can help Team-FTU.txt 241 Bytes13.4 - How to represent a dataset as a Matrix/How you can help Team-FTU.txt 241 Bytes13.5 - Data Preprocessing Feature Normalisation/How you can help Team-FTU.txt 241 Bytes13.6 - Mean of a data matrix/How you can help Team-FTU.txt 241 Bytes13.7 - Data Preprocessing Column Standardization/How you can help Team-FTU.txt 241 Bytes13.8 - Co-variance of a Data Matrix/How you can help Team-FTU.txt 241 Bytes13.9 - MNIST dataset (784 dimensional)/How you can help Team-FTU.txt 241 Bytes14.1 - Why learn PCA/How you can help Team-FTU.txt 241 Bytes14.10 - PCA for dimensionality reduction (not-visualization)/How you can help Team-FTU.txt 241 Bytes14.2 - Geometric intuition of PCA/How you can help Team-FTU.txt 241 Bytes14.3 - Mathematical objective function of PCA/How you can help Team-FTU.txt 241 Bytes14.4 - Alternative formulation of PCA Distance minimization/How you can help Team-FTU.txt 241 Bytes14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/How you can help Team-FTU.txt 241 Bytes14.6 - PCA for Dimensionality Reduction and Visualization/How you can help Team-FTU.txt 241 Bytes14.7 - Visualize MNIST dataset/How you can help Team-FTU.txt 241 Bytes14.8 - Limitations of PCA/How you can help Team-FTU.txt 241 Bytes14.9 - PCA Code example/How you can help Team-FTU.txt 241 Bytes15.1 - What is t-SNE/How you can help Team-FTU.txt 241 Bytes15.2 - Neighborhood of a point, Embedding/How you can help Team-FTU.txt 241 Bytes15.3 - Geometric intuition of t-SNE/How you can help Team-FTU.txt 241 Bytes15.4 - Crowding Problem/How you can help Team-FTU.txt 241 Bytes15.5 - How to apply t-SNE and interpret its output/How you can help Team-FTU.txt 241 Bytes15.6 - t-SNE on MNIST/How you can help Team-FTU.txt 241 Bytes15.7 - Code example of t-SNE/How you can help Team-FTU.txt 241 Bytes15.8 - Revision Questions/How you can help Team-FTU.txt 241 Bytes16.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes17.1 - Dataset overview Amazon Fine Food reviews(EDA)/How you can help Team-FTU.txt 241 Bytes17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/How you can help Team-FTU.txt 241 Bytes17.11 - Bag of Words( Code Sample)/How you can help Team-FTU.txt 241 Bytes17.12 - Text Preprocessing( Code Sample)/How you can help Team-FTU.txt 241 Bytes17.13 - Bi-Grams and n-grams (Code Sample)/How you can help Team-FTU.txt 241 Bytes17.14 - TF-IDF (Code Sample)/How you can help Team-FTU.txt 241 Bytes17.15 - Word2Vec (Code Sample)/How you can help Team-FTU.txt 241 Bytes17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/How you can help Team-FTU.txt 241 Bytes17.17 - Assignment-2 Apply t-SNE/How you can help Team-FTU.txt 241 Bytes17.2 - Data Cleaning Deduplication/How you can help Team-FTU.txt 241 Bytes17.3 - Why convert text to a vector/How you can help Team-FTU.txt 241 Bytes17.4 - Bag of Words (BoW)/How you can help Team-FTU.txt 241 Bytes17.5 - Text Preprocessing Stemming/How you can help Team-FTU.txt 241 Bytes17.6 - uni-gram, bi-gram, n-grams/How you can help Team-FTU.txt 241 Bytes17.7 - tf-idf (term frequency- inverse document frequency)/How you can help Team-FTU.txt 241 Bytes17.8 - Why use log in IDF/How you can help Team-FTU.txt 241 Bytes17.9 - Word2Vec/How you can help Team-FTU.txt 241 Bytes18.1 - How “Classification” works/How you can help Team-FTU.txt 241 Bytes18.10 - KNN Limitations/How you can help Team-FTU.txt 241 Bytes18.11 - Decision surface for K-NN as K changes/How you can help Team-FTU.txt 241 Bytes18.12 - Overfitting and Underfitting/How you can help Team-FTU.txt 241 Bytes18.13 - Need for Cross validation/How you can help Team-FTU.txt 241 Bytes18.14 - K-fold cross validation/How you can help Team-FTU.txt 241 Bytes18.15 - Visualizing train, validation and test datasets/How you can help Team-FTU.txt 241 Bytes18.16 - How to determine overfitting and underfitting/How you can help Team-FTU.txt 241 Bytes18.17 - Time based splitting/How you can help Team-FTU.txt 241 Bytes18.18 - k-NN for regression/How you can help Team-FTU.txt 241 Bytes18.19 - Weighted k-NN/How you can help Team-FTU.txt 241 Bytes18.2 - Data matrix notation/How you can help Team-FTU.txt 241 Bytes18.20 - Voronoi diagram/How you can help Team-FTU.txt 241 Bytes18.21 - Binary search tree/How you can help Team-FTU.txt 241 Bytes18.22 - How to build a kd-tree/How you can help Team-FTU.txt 241 Bytes18.23 - Find nearest neighbours using kd-tree/How you can help Team-FTU.txt 241 Bytes18.24 - Limitations of Kd tree/How you can help Team-FTU.txt 241 Bytes18.25 - Extensions/How you can help Team-FTU.txt 241 Bytes18.26 - Hashing vs LSH/How you can help Team-FTU.txt 241 Bytes18.27 - LSH for cosine similarity/How you can help Team-FTU.txt 241 Bytes18.28 - LSH for euclidean distance/How you can help Team-FTU.txt 241 Bytes18.29 - Probabilistic class label/How you can help Team-FTU.txt 241 Bytes18.3 - Classification vs Regression (examples)/How you can help Team-FTU.txt 241 Bytes18.30 - Code SampleDecision boundary/How you can help Team-FTU.txt 241 Bytes18.31 - Code SampleCross Validation/How you can help Team-FTU.txt 241 Bytes18.32 - Revision Questions/How you can help Team-FTU.txt 241 Bytes18.4 - K-Nearest Neighbours Geometric intuition with a toy example/How you can help Team-FTU.txt 241 Bytes18.5 - Failure cases of KNN/How you can help Team-FTU.txt 241 Bytes18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/How you can help Team-FTU.txt 241 Bytes18.7 - Cosine Distance & Cosine Similarity/How you can help Team-FTU.txt 241 Bytes18.8 - How to measure the effectiveness of k-NN/How you can help Team-FTU.txt 241 Bytes18.9 - TestEvaluation time and space complexity/How you can help Team-FTU.txt 241 Bytes19.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes2.1 - Python, Anaconda and relevant packages installations/How you can help Team-FTU.txt 241 Bytes2.10 - Control flow for loop/How you can help Team-FTU.txt 241 Bytes2.11 - Control flow break and continue/How you can help Team-FTU.txt 241 Bytes2.2 - Why learn Python/How you can help Team-FTU.txt 241 Bytes2.3 - Keywords and identifiers/How you can help Team-FTU.txt 241 Bytes2.4 - comments, indentation and statements/How you can help Team-FTU.txt 241 Bytes2.5 - Variables and data types in Python/How you can help Team-FTU.txt 241 Bytes2.6 - Standard Input and Output/How you can help Team-FTU.txt 241 Bytes2.7 - Operators/How you can help Team-FTU.txt 241 Bytes2.8 - Control flow if else/How you can help Team-FTU.txt 241 Bytes2.9 - Control flow while loop/How you can help Team-FTU.txt 241 Bytes20.1 - Introduction/How you can help Team-FTU.txt 241 Bytes20.10 - Local reachability-density(A)/How you can help Team-FTU.txt 241 Bytes20.11 - Local outlier Factor(A)/How you can help Team-FTU.txt 241 Bytes20.12 - Impact of Scale & Column standardization/How you can help Team-FTU.txt 241 Bytes20.13 - Interpretability/How you can help Team-FTU.txt 241 Bytes20.14 - Feature Importance and Forward Feature selection/How you can help Team-FTU.txt 241 Bytes20.15 - Handling categorical and numerical features/How you can help Team-FTU.txt 241 Bytes20.16 - Handling missing values by imputation/How you can help Team-FTU.txt 241 Bytes20.17 - curse of dimensionality/How you can help Team-FTU.txt 241 Bytes20.18 - Bias-Variance tradeoff/How you can help Team-FTU.txt 241 Bytes20.19 - Intuitive understanding of bias-variance/How you can help Team-FTU.txt 241 Bytes20.2 - Imbalanced vs balanced dataset/How you can help Team-FTU.txt 241 Bytes20.20 - Revision Questions/How you can help Team-FTU.txt 241 Bytes20.21 - best and wrost case of algorithm/How you can help Team-FTU.txt 241 Bytes20.3 - Multi-class classification/How you can help Team-FTU.txt 241 Bytes20.4 - k-NN, given a distance or similarity matrix/How you can help Team-FTU.txt 241 Bytes20.5 - Train and test set differences/How you can help Team-FTU.txt 241 Bytes20.6 - Impact of outliers/How you can help Team-FTU.txt 241 Bytes20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/How you can help Team-FTU.txt 241 Bytes20.8 - k distance/How you can help Team-FTU.txt 241 Bytes20.9 - Reachability-Distance(A,B)/How you can help Team-FTU.txt 241 Bytes21.1 - Accuracy/How you can help Team-FTU.txt 241 Bytes21.10 - Revision Questions/How you can help Team-FTU.txt 241 Bytes21.2 - Confusion matrix, TPR, FPR, FNR, TNR/How you can help Team-FTU.txt 241 Bytes21.3 - Precision and recall, F1-score/How you can help Team-FTU.txt 241 Bytes21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/How you can help Team-FTU.txt 241 Bytes21.5 - Log-loss/How you can help Team-FTU.txt 241 Bytes21.6 - R-SquaredCoefficient of determination/How you can help Team-FTU.txt 241 Bytes21.7 - Median absolute deviation (MAD)/How you can help Team-FTU.txt 241 Bytes21.8 - Distribution of errors/How you can help Team-FTU.txt 241 Bytes21.9 - Assignment-3 Apply k-Nearest Neighbor/How you can help Team-FTU.txt 241 Bytes22.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes23.1 - Conditional probability/How you can help Team-FTU.txt 241 Bytes23.10 - Bias and Variance tradeoff/How you can help Team-FTU.txt 241 Bytes23.11 - Feature importance and interpretability/How you can help Team-FTU.txt 241 Bytes23.12 - Imbalanced data/How you can help Team-FTU.txt 241 Bytes23.13 - Outliers/How you can help Team-FTU.txt 241 Bytes23.14 - Missing values/How you can help Team-FTU.txt 241 Bytes23.15 - Handling Numerical features (Gaussian NB)/How you can help Team-FTU.txt 241 Bytes23.16 - Multiclass classification/How you can help Team-FTU.txt 241 Bytes23.17 - Similarity or Distance matrix/How you can help Team-FTU.txt 241 Bytes23.18 - Large dimensionality/How you can help Team-FTU.txt 241 Bytes23.19 - Best and worst cases/How you can help Team-FTU.txt 241 Bytes23.2 - Independent vs Mutually exclusive events/How you can help Team-FTU.txt 241 Bytes23.20 - Code example/How you can help Team-FTU.txt 241 Bytes23.21 - Assignment-4 Apply Naive Bayes/How you can help Team-FTU.txt 241 Bytes23.22 - Revision Questions/How you can help Team-FTU.txt 241 Bytes23.3 - Bayes Theorem with examples/How you can help Team-FTU.txt 241 Bytes23.4 - Exercise problems on Bayes Theorem/How you can help Team-FTU.txt 241 Bytes23.5 - Naive Bayes algorithm/How you can help Team-FTU.txt 241 Bytes23.6 - Toy example Train and test stages/How you can help Team-FTU.txt 241 Bytes23.7 - Naive Bayes on Text data/How you can help Team-FTU.txt 241 Bytes23.8 - LaplaceAdditive Smoothing/How you can help Team-FTU.txt 241 Bytes23.9 - Log-probabilities for numerical stability/How you can help Team-FTU.txt 241 Bytes24.1 - Geometric intuition of Logistic Regression/How you can help Team-FTU.txt 241 Bytes24.10 - Column Standardization/How you can help Team-FTU.txt 241 Bytes24.11 - Feature importance and Model interpretability/How you can help Team-FTU.txt 241 Bytes24.12 - Collinearity of features/How you can help Team-FTU.txt 241 Bytes24.13 - TestRun time space and time complexity/How you can help Team-FTU.txt 241 Bytes24.14 - Real world cases/How you can help Team-FTU.txt 241 Bytes24.15 - Non-linearly separable data & feature engineering/How you can help Team-FTU.txt 241 Bytes24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/How you can help Team-FTU.txt 241 Bytes24.17 - Assignment-5 Apply Logistic Regression/How you can help Team-FTU.txt 241 Bytes24.18 - Extensions to Generalized linear models/How you can help Team-FTU.txt 241 Bytes24.2 - Sigmoid function Squashing/How you can help Team-FTU.txt 241 Bytes24.3 - Mathematical formulation of Objective function/How you can help Team-FTU.txt 241 Bytes24.4 - Weight vector/How you can help Team-FTU.txt 241 Bytes24.5 - L2 Regularization Overfitting and Underfitting/How you can help Team-FTU.txt 241 Bytes24.6 - L1 regularization and sparsity/How you can help Team-FTU.txt 241 Bytes24.7 - Probabilistic Interpretation Gaussian Naive Bayes/How you can help Team-FTU.txt 241 Bytes24.8 - Loss minimization interpretation/How you can help Team-FTU.txt 241 Bytes24.9 - hyperparameters and random search/How you can help Team-FTU.txt 241 Bytes25.1 - Geometric intuition of Linear Regression/How you can help Team-FTU.txt 241 Bytes25.2 - Mathematical formulation/How you can help Team-FTU.txt 241 Bytes25.3 - Real world Cases/How you can help Team-FTU.txt 241 Bytes25.4 - Code sample for Linear Regression/How you can help Team-FTU.txt 241 Bytes26.1 - Differentiation/How you can help Team-FTU.txt 241 Bytes26.10 - Logistic regression formulation revisited/How you can help Team-FTU.txt 241 Bytes26.11 - Why L1 regularization creates sparsity/How you can help Team-FTU.txt 241 Bytes26.12 - Assignment 6 Implement SGD for linear regression/How you can help Team-FTU.txt 241 Bytes26.13 - Revision questions/How you can help Team-FTU.txt 241 Bytes26.2 - Online differentiation tools/How you can help Team-FTU.txt 241 Bytes26.3 - Maxima and Minima/How you can help Team-FTU.txt 241 Bytes26.4 - Vector calculus Grad/How you can help Team-FTU.txt 241 Bytes26.5 - Gradient descent geometric intuition/How you can help Team-FTU.txt 241 Bytes26.6 - Learning rate/How you can help Team-FTU.txt 241 Bytes26.7 - Gradient descent for linear regression/How you can help Team-FTU.txt 241 Bytes26.8 - SGD algorithm/How you can help Team-FTU.txt 241 Bytes26.9 - Constrained Optimization & PCA/How you can help Team-FTU.txt 241 Bytes27.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes28.1 - Geometric Intution/How you can help Team-FTU.txt 241 Bytes28.10 - Train and run time complexities/How you can help Team-FTU.txt 241 Bytes28.11 - nu-SVM control errors and support vectors/How you can help Team-FTU.txt 241 Bytes28.12 - SVM Regression/How you can help Team-FTU.txt 241 Bytes28.13 - Cases/How you can help Team-FTU.txt 241 Bytes28.14 - Code Sample/How you can help Team-FTU.txt 241 Bytes28.15 - Assignment-7 Apply SVM/How you can help Team-FTU.txt 241 Bytes28.16 - Revision Questions/How you can help Team-FTU.txt 241 Bytes28.2 - Mathematical derivation/How you can help Team-FTU.txt 241 Bytes28.3 - Why we take values +1 and and -1 for Support vector planes/How you can help Team-FTU.txt 241 Bytes28.4 - Loss function (Hinge Loss) based interpretation/How you can help Team-FTU.txt 241 Bytes28.5 - Dual form of SVM formulation/How you can help Team-FTU.txt 241 Bytes28.6 - kernel trick/How you can help Team-FTU.txt 241 Bytes28.7 - Polynomial Kernel/How you can help Team-FTU.txt 241 Bytes28.8 - RBF-Kernel/How you can help Team-FTU.txt 241 Bytes28.9 - Domain specific Kernels/How you can help Team-FTU.txt 241 Bytes29.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes3.1 - Lists/How you can help Team-FTU.txt 241 Bytes3.2 - Tuples part 1/How you can help Team-FTU.txt 241 Bytes3.3 - Tuples part-2/How you can help Team-FTU.txt 241 Bytes3.4 - Sets/How you can help Team-FTU.txt 241 Bytes3.5 - Dictionary/How you can help Team-FTU.txt 241 Bytes3.6 - Strings/How you can help Team-FTU.txt 241 Bytes30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/How you can help Team-FTU.txt 241 Bytes30.10 - Overfitting and Underfitting/How you can help Team-FTU.txt 241 Bytes30.11 - Train and Run time complexity/How you can help Team-FTU.txt 241 Bytes30.12 - Regression using Decision Trees/How you can help Team-FTU.txt 241 Bytes30.13 - Cases/How you can help Team-FTU.txt 241 Bytes30.14 - Code Samples/How you can help Team-FTU.txt 241 Bytes30.15 - Assignment-8 Apply Decision Trees/How you can help Team-FTU.txt 241 Bytes30.16 - Revision Questions/How you can help Team-FTU.txt 241 Bytes30.2 - Sample Decision tree/How you can help Team-FTU.txt 241 Bytes30.3 - Building a decision TreeEntropy/How you can help Team-FTU.txt 241 Bytes30.4 - Building a decision TreeInformation Gain/How you can help Team-FTU.txt 241 Bytes30.5 - Building a decision Tree Gini Impurity/How you can help Team-FTU.txt 241 Bytes30.6 - Building a decision Tree Constructing a DT/How you can help Team-FTU.txt 241 Bytes30.7 - Building a decision Tree Splitting numerical features/How you can help Team-FTU.txt 241 Bytes30.8 - Feature standardization/How you can help Team-FTU.txt 241 Bytes30.9 - Building a decision TreeCategorical features with many possible values/How you can help Team-FTU.txt 241 Bytes31.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes32.1 - What are ensembles/How you can help Team-FTU.txt 241 Bytes32.10 - Residuals, Loss functions and gradients/How you can help Team-FTU.txt 241 Bytes32.11 - Gradient Boosting/How you can help Team-FTU.txt 241 Bytes32.12 - Regularization by Shrinkage/How you can help Team-FTU.txt 241 Bytes32.13 - Train and Run time complexity/How you can help Team-FTU.txt 241 Bytes32.14 - XGBoost Boosting + Randomization/How you can help Team-FTU.txt 241 Bytes32.15 - AdaBoost geometric intuition/How you can help Team-FTU.txt 241 Bytes32.16 - Stacking models/How you can help Team-FTU.txt 241 Bytes32.17 - Cascading classifiers/How you can help Team-FTU.txt 241 Bytes32.18 - Kaggle competitions vs Real world/How you can help Team-FTU.txt 241 Bytes32.19 - Assignment-9 Apply Random Forests & GBDT/How you can help Team-FTU.txt 241 Bytes32.2 - Bootstrapped Aggregation (Bagging) Intuition/How you can help Team-FTU.txt 241 Bytes32.20 - Revision Questions/How you can help Team-FTU.txt 241 Bytes32.3 - Random Forest and their construction/How you can help Team-FTU.txt 241 Bytes32.4 - Bias-Variance tradeoff/How you can help Team-FTU.txt 241 Bytes32.5 - Train and run time complexity/How you can help Team-FTU.txt 241 Bytes32.6 - BaggingCode Sample/How you can help Team-FTU.txt 241 Bytes32.7 - Extremely randomized trees/How you can help Team-FTU.txt 241 Bytes32.8 - Random Tree Cases/How you can help Team-FTU.txt 241 Bytes32.9 - Boosting Intuition/How you can help Team-FTU.txt 241 Bytes33.1 - Introduction/How you can help Team-FTU.txt 241 Bytes33.10 - Indicator variables/How you can help Team-FTU.txt 241 Bytes33.11 - Feature binning/How you can help Team-FTU.txt 241 Bytes33.12 - Interaction variables/How you can help Team-FTU.txt 241 Bytes33.13 - Mathematical transforms/How you can help Team-FTU.txt 241 Bytes33.14 - Model specific featurizations/How you can help Team-FTU.txt 241 Bytes33.15 - Feature orthogonality/How you can help Team-FTU.txt 241 Bytes33.16 - Domain specific featurizations/How you can help Team-FTU.txt 241 Bytes33.17 - Feature slicing/How you can help Team-FTU.txt 241 Bytes33.18 - Kaggle Winners solutions/How you can help Team-FTU.txt 241 Bytes33.2 - Moving window for Time Series Data/How you can help Team-FTU.txt 241 Bytes33.3 - Fourier decomposition/How you can help Team-FTU.txt 241 Bytes33.4 - Deep learning features LSTM/How you can help Team-FTU.txt 241 Bytes33.5 - Image histogram/How you can help Team-FTU.txt 241 Bytes33.6 - Keypoints SIFT/How you can help Team-FTU.txt 241 Bytes33.7 - Deep learning features CNN/How you can help Team-FTU.txt 241 Bytes33.8 - Relational data/How you can help Team-FTU.txt 241 Bytes33.9 - Graph data/How you can help Team-FTU.txt 241 Bytes34.1 - Calibration of ModelsNeed for calibration/How you can help Team-FTU.txt 241 Bytes34.10 - AB testing/How you can help Team-FTU.txt 241 Bytes34.11 - Data Science Life cycle/How you can help Team-FTU.txt 241 Bytes34.12 - VC dimension/How you can help Team-FTU.txt 241 Bytes34.2 - Productionization and deployment of Machine Learning Models/How you can help Team-FTU.txt 241 Bytes34.3 - Calibration Plots/How you can help Team-FTU.txt 241 Bytes34.4 - Platt’s CalibrationScaling/How you can help Team-FTU.txt 241 Bytes34.5 - Isotonic Regression/How you can help Team-FTU.txt 241 Bytes34.6 - Code Samples/How you can help Team-FTU.txt 241 Bytes34.7 - Modeling in the presence of outliers RANSAC/How you can help Team-FTU.txt 241 Bytes34.8 - Productionizing models/How you can help Team-FTU.txt 241 Bytes34.9 - Retraining models periodically/How you can help Team-FTU.txt 241 Bytes35.1 - What is Clustering/How you can help Team-FTU.txt 241 Bytes35.10 - K-Medoids/How you can help Team-FTU.txt 241 Bytes35.11 - Determining the right K/How you can help Team-FTU.txt 241 Bytes35.12 - Code Samples/How you can help Team-FTU.txt 241 Bytes35.13 - Time and space complexity/How you can help Team-FTU.txt 241 Bytes35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/How you can help Team-FTU.txt 241 Bytes35.2 - Unsupervised learning/How you can help Team-FTU.txt 241 Bytes35.3 - Applications/How you can help Team-FTU.txt 241 Bytes35.4 - Metrics for Clustering/How you can help Team-FTU.txt 241 Bytes35.5 - K-Means Geometric intuition, Centroids/How you can help Team-FTU.txt 241 Bytes35.6 - K-Means Mathematical formulation Objective function/How you can help Team-FTU.txt 241 Bytes35.7 - K-Means Algorithm/How you can help Team-FTU.txt 241 Bytes35.8 - How to initialize K-Means++/How you can help Team-FTU.txt 241 Bytes35.9 - Failure casesLimitations/How you can help Team-FTU.txt 241 Bytes36.1 - Agglomerative & Divisive, Dendrograms/How you can help Team-FTU.txt 241 Bytes36.2 - Agglomerative Clustering/How you can help Team-FTU.txt 241 Bytes36.3 - Proximity methods Advantages and Limitations/How you can help Team-FTU.txt 241 Bytes36.4 - Time and Space Complexity/How you can help Team-FTU.txt 241 Bytes36.5 - Limitations of Hierarchical Clustering/How you can help Team-FTU.txt 241 Bytes36.6 - Code sample/How you can help Team-FTU.txt 241 Bytes36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/How you can help Team-FTU.txt 241 Bytes37.1 - Density based clustering/How you can help Team-FTU.txt 241 Bytes37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/How you can help Team-FTU.txt 241 Bytes37.11 - Revision Questions/How you can help Team-FTU.txt 241 Bytes37.2 - MinPts and Eps Density/How you can help Team-FTU.txt 241 Bytes37.3 - Core, Border and Noise points/How you can help Team-FTU.txt 241 Bytes37.4 - Density edge and Density connected points/How you can help Team-FTU.txt 241 Bytes37.5 - DBSCAN Algorithm/How you can help Team-FTU.txt 241 Bytes37.6 - Hyper Parameters MinPts and Eps/How you can help Team-FTU.txt 241 Bytes37.7 - Advantages and Limitations of DBSCAN/How you can help Team-FTU.txt 241 Bytes37.8 - Time and Space Complexity/How you can help Team-FTU.txt 241 Bytes37.9 - Code samples/How you can help Team-FTU.txt 241 Bytes38.1 - Problem formulation Movie reviews/How you can help Team-FTU.txt 241 Bytes38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/How you can help Team-FTU.txt 241 Bytes38.11 - Cold Start problem/How you can help Team-FTU.txt 241 Bytes38.12 - Word vectors as MF/How you can help Team-FTU.txt 241 Bytes38.13 - Eigen-Faces/How you can help Team-FTU.txt 241 Bytes38.14 - Code example/How you can help Team-FTU.txt 241 Bytes38.15 - Assignment-11 Apply Truncated SVD/How you can help Team-FTU.txt 241 Bytes38.16 - Revision Questions/How you can help Team-FTU.txt 241 Bytes38.2 - Content based vs Collaborative Filtering/How you can help Team-FTU.txt 241 Bytes38.3 - Similarity based Algorithms/How you can help Team-FTU.txt 241 Bytes38.4 - Matrix Factorization PCA, SVD/How you can help Team-FTU.txt 241 Bytes38.5 - Matrix Factorization NMF/How you can help Team-FTU.txt 241 Bytes38.6 - Matrix Factorization for Collaborative filtering/How you can help Team-FTU.txt 241 Bytes38.7 - Matrix Factorization for feature engineering/How you can help Team-FTU.txt 241 Bytes38.8 - Clustering as MF/How you can help Team-FTU.txt 241 Bytes38.9 - Hyperparameter tuning/How you can help Team-FTU.txt 241 Bytes39.1 - Questions & Answers/How you can help Team-FTU.txt 241 Bytes4.1 - Introduction/How you can help Team-FTU.txt 241 Bytes4.10 - Debugging Python/How you can help Team-FTU.txt 241 Bytes4.2 - Types of functions/How you can help Team-FTU.txt 241 Bytes4.3 - Function arguments/How you can help Team-FTU.txt 241 Bytes4.4 - Recursive functions/How you can help Team-FTU.txt 241 Bytes4.5 - Lambda functions/How you can help Team-FTU.txt 241 Bytes4.6 - Modules/How you can help Team-FTU.txt 241 Bytes4.7 - Packages/How you can help Team-FTU.txt 241 Bytes4.8 - File Handling/How you can help Team-FTU.txt 241 Bytes4.9 - Exception Handling/How you can help Team-FTU.txt 241 Bytes40.1 - BusinessReal world problem/How you can help Team-FTU.txt 241 Bytes40.10 - Data Modeling Multi label Classification/How you can help Team-FTU.txt 241 Bytes40.11 - Data preparation/How you can help Team-FTU.txt 241 Bytes40.12 - Train-Test Split/How you can help Team-FTU.txt 241 Bytes40.13 - Featurization/How you can help Team-FTU.txt 241 Bytes40.14 - Logistic regression One VS Rest/How you can help Team-FTU.txt 241 Bytes40.15 - Sampling data and tags+Weighted models/How you can help Team-FTU.txt 241 Bytes40.16 - Logistic regression revisited/How you can help Team-FTU.txt 241 Bytes40.17 - Why not use advanced techniques/How you can help Team-FTU.txt 241 Bytes40.18 - Assignments/How you can help Team-FTU.txt 241 Bytes40.2 - Business objectives and constraints/How you can help Team-FTU.txt 241 Bytes40.3 - Mapping to an ML problem Data overview/How you can help Team-FTU.txt 241 Bytes40.4 - Mapping to an ML problemML problem formulation/How you can help Team-FTU.txt 241 Bytes40.5 - Mapping to an ML problemPerformance metrics/How you can help Team-FTU.txt 241 Bytes40.6 - Hamming loss/How you can help Team-FTU.txt 241 Bytes40.7 - EDAData Loading/How you can help Team-FTU.txt 241 Bytes40.8 - EDAAnalysis of tags/How you can help Team-FTU.txt 241 Bytes40.9 - EDAData Preprocessing/How you can help Team-FTU.txt 241 Bytes41.1 - BusinessReal world problem Problem definition/How you can help Team-FTU.txt 241 Bytes41.10 - EDA Feature analysis/How you can help Team-FTU.txt 241 Bytes41.11 - EDA Data Visualization T-SNE/How you can help Team-FTU.txt 241 Bytes41.12 - EDA TF-IDF weighted Word2Vec featurization/How you can help Team-FTU.txt 241 Bytes41.13 - ML Models Loading Data/How you can help Team-FTU.txt 241 Bytes41.14 - ML Models Random Model/How you can help Team-FTU.txt 241 Bytes41.15 - ML Models Logistic Regression and Linear SVM/How you can help Team-FTU.txt 241 Bytes41.16 - ML Models XGBoost/How you can help Team-FTU.txt 241 Bytes41.17 - Assignments/How you can help Team-FTU.txt 241 Bytes41.2 - Business objectives and constraints/How you can help Team-FTU.txt 241 Bytes41.3 - Mapping to an ML problem Data overview/How you can help Team-FTU.txt 241 Bytes41.4 - Mapping to an ML problem ML problem and performance metric/How you can help Team-FTU.txt 241 Bytes41.5 - Mapping to an ML problem Train-test split/How you can help Team-FTU.txt 241 Bytes41.6 - EDA Basic Statistics/How you can help Team-FTU.txt 241 Bytes41.7 - EDA Basic Feature Extraction/How you can help Team-FTU.txt 241 Bytes41.8 - EDA Text Preprocessing/How you can help Team-FTU.txt 241 Bytes41.9 - EDA Advanced Feature Extraction/How you can help Team-FTU.txt 241 Bytes42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/How you can help Team-FTU.txt 241 Bytes42.10 - Text Pre-Processing Tokenization and Stop-word removal/How you can help Team-FTU.txt 241 Bytes42.11 - Stemming/How you can help Team-FTU.txt 241 Bytes42.12 - Text based product similarity Converting text to an n-D vector bag of words/How you can help Team-FTU.txt 241 Bytes42.13 - Code for bag of words based product similarity/How you can help Team-FTU.txt 241 Bytes42.14 - TF-IDF featurizing text based on word-importance/How you can help Team-FTU.txt 241 Bytes42.15 - Code for TF-IDF based product similarity/How you can help Team-FTU.txt 241 Bytes42.16 - Code for IDF based product similarity/How you can help Team-FTU.txt 241 Bytes42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/How you can help Team-FTU.txt 241 Bytes42.18 - Code for Average Word2Vec product similarity/How you can help Team-FTU.txt 241 Bytes42.19 - TF-IDF weighted Word2Vec/How you can help Team-FTU.txt 241 Bytes42.2 - Plan of action/How you can help Team-FTU.txt 241 Bytes42.20 - Code for IDF weighted Word2Vec product similarity/How you can help Team-FTU.txt 241 Bytes42.21 - Weighted similarity using brand and color/How you can help Team-FTU.txt 241 Bytes42.22 - Code for weighted similarity/How you can help Team-FTU.txt 241 Bytes42.23 - Building a real world solution/How you can help Team-FTU.txt 241 Bytes42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/How you can help Team-FTU.txt 241 Bytes42.25 - Using Keras + Tensorflow to extract features/How you can help Team-FTU.txt 241 Bytes42.26 - Visual similarity based product similarity/How you can help Team-FTU.txt 241 Bytes42.27 - Measuring goodness of our solution AB testing/How you can help Team-FTU.txt 241 Bytes42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/How you can help Team-FTU.txt 241 Bytes42.3 - Amazon product advertising API/How you can help Team-FTU.txt 241 Bytes42.4 - Data folders and paths/How you can help Team-FTU.txt 241 Bytes42.5 - Overview of the data and Terminology/How you can help Team-FTU.txt 241 Bytes42.6 - Data cleaning and understandingMissing data in various features/How you can help Team-FTU.txt 241 Bytes42.7 - Understand duplicate rows/How you can help Team-FTU.txt 241 Bytes42.8 - Remove duplicates Part 1/How you can help Team-FTU.txt 241 Bytes42.9 - Remove duplicates Part 2/How you can help Team-FTU.txt 241 Bytes43.1 - Businessreal world problem Problem definition/How you can help Team-FTU.txt 241 Bytes43.10 - ML models – using byte files only Random Model/How you can help Team-FTU.txt 241 Bytes43.11 - k-NN/How you can help Team-FTU.txt 241 Bytes43.12 - Logistic regression/How you can help Team-FTU.txt 241 Bytes43.13 - Random Forest and Xgboost/How you can help Team-FTU.txt 241 Bytes43.14 - ASM Files Feature extraction & Multiprocessing/How you can help Team-FTU.txt 241 Bytes43.15 - File-size feature/How you can help Team-FTU.txt 241 Bytes43.16 - Univariate analysis/How you can help Team-FTU.txt 241 Bytes43.17 - t-SNE analysis/How you can help Team-FTU.txt 241 Bytes43.18 - ML models on ASM file features/How you can help Team-FTU.txt 241 Bytes43.19 - Models on all features t-SNE/How you can help Team-FTU.txt 241 Bytes43.2 - Businessreal world problem Objectives and constraints/How you can help Team-FTU.txt 241 Bytes43.20 - Models on all features RandomForest and Xgboost/How you can help Team-FTU.txt 241 Bytes43.21 - Assignments/How you can help Team-FTU.txt 241 Bytes43.3 - Machine Learning problem mapping Data overview/How you can help Team-FTU.txt 241 Bytes43.4 - Machine Learning problem mapping ML problem/How you can help Team-FTU.txt 241 Bytes43.5 - Machine Learning problem mapping Train and test splitting/How you can help Team-FTU.txt 241 Bytes43.6 - Exploratory Data Analysis Class distribution/How you can help Team-FTU.txt 241 Bytes43.7 - Exploratory Data Analysis Feature extraction from byte files/How you can help Team-FTU.txt 241 Bytes43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/How you can help Team-FTU.txt 241 Bytes43.9 - Exploratory Data Analysis Train-Test class distribution/How you can help Team-FTU.txt 241 Bytes44.1 - BusinessReal world problemProblem definition/How you can help Team-FTU.txt 241 Bytes44.10 - Exploratory Data AnalysisCold start problem/How you can help Team-FTU.txt 241 Bytes44.11 - Computing Similarity matricesUser-User similarity matrix/How you can help Team-FTU.txt 241 Bytes44.12 - Computing Similarity matricesMovie-Movie similarity/How you can help Team-FTU.txt 241 Bytes44.13 - Computing Similarity matricesDoes movie-movie similarity work/How you can help Team-FTU.txt 241 Bytes44.14 - ML ModelsSurprise library/How you can help Team-FTU.txt 241 Bytes44.15 - Overview of the modelling strategy/How you can help Team-FTU.txt 241 Bytes44.16 - Data Sampling/How you can help Team-FTU.txt 241 Bytes44.17 - Google drive with intermediate files/How you can help Team-FTU.txt 241 Bytes44.18 - Featurizations for regression/How you can help Team-FTU.txt 241 Bytes44.19 - Data transformation for Surprise/How you can help Team-FTU.txt 241 Bytes44.2 - Objectives and constraints/How you can help Team-FTU.txt 241 Bytes44.20 - Xgboost with 13 features/How you can help Team-FTU.txt 241 Bytes44.21 - Surprise Baseline model/How you can help Team-FTU.txt 241 Bytes44.22 - Xgboost + 13 features +Surprise baseline model/How you can help Team-FTU.txt 241 Bytes44.23 - Surprise KNN predictors/How you can help Team-FTU.txt 241 Bytes44.24 - Matrix Factorization models using Surprise/How you can help Team-FTU.txt 241 Bytes44.25 - SVD ++ with implicit feedback/How you can help Team-FTU.txt 241 Bytes44.26 - Final models with all features and predictors/How you can help Team-FTU.txt 241 Bytes44.27 - Comparison between various models/How you can help Team-FTU.txt 241 Bytes44.28 - Assignments/How you can help Team-FTU.txt 241 Bytes44.3 - Mapping to an ML problemData overview/How you can help Team-FTU.txt 241 Bytes44.4 - Mapping to an ML problemML problem formulation/How you can help Team-FTU.txt 241 Bytes44.5 - Exploratory Data AnalysisData preprocessing/How you can help Team-FTU.txt 241 Bytes44.6 - Exploratory Data AnalysisTemporal Train-Test split/How you can help Team-FTU.txt 241 Bytes44.7 - Exploratory Data AnalysisPreliminary data analysis/How you can help Team-FTU.txt 241 Bytes44.8 - Exploratory Data AnalysisSparse matrix representation/How you can help Team-FTU.txt 241 Bytes44.9 - Exploratory Data AnalysisAverage ratings for various slices/How you can help Team-FTU.txt 241 Bytes45.1 - BusinessReal world problem Overview/How you can help Team-FTU.txt 241 Bytes45.10 - Univariate AnalysisVariation Feature/How you can help Team-FTU.txt 241 Bytes45.11 - Univariate AnalysisText feature/How you can help Team-FTU.txt 241 Bytes45.12 - Machine Learning ModelsData preparation/How you can help Team-FTU.txt 241 Bytes45.13 - Baseline Model Naive Bayes/How you can help Team-FTU.txt 241 Bytes45.14 - K-Nearest Neighbors Classification/How you can help Team-FTU.txt 241 Bytes45.15 - Logistic Regression with class balancing/How you can help Team-FTU.txt 241 Bytes45.16 - Logistic Regression without class balancing/How you can help Team-FTU.txt 241 Bytes45.17 - Linear-SVM/How you can help Team-FTU.txt 241 Bytes45.18 - Random-Forest with one-hot encoded features/How you can help Team-FTU.txt 241 Bytes45.19 - Random-Forest with response-coded features/How you can help Team-FTU.txt 241 Bytes45.2 - Business objectives and constraints/How you can help Team-FTU.txt 241 Bytes45.20 - Stacking Classifier/How you can help Team-FTU.txt 241 Bytes45.21 - Majority Voting classifier/How you can help Team-FTU.txt 241 Bytes45.22 - Assignments/How you can help Team-FTU.txt 241 Bytes45.3 - ML problem formulation Data/How you can help Team-FTU.txt 241 Bytes45.4 - ML problem formulation Mapping real world to ML problem#/How you can help Team-FTU.txt 241 Bytes45.4 - ML problem formulation Mapping real world to ML problem/How you can help Team-FTU.txt 241 Bytes45.5 - ML problem formulation Train, CV and Test data construction/How you can help Team-FTU.txt 241 Bytes45.6 - Exploratory Data AnalysisReading data & preprocessing/How you can help Team-FTU.txt 241 Bytes45.7 - Exploratory Data AnalysisDistribution of Class-labels/How you can help Team-FTU.txt 241 Bytes45.8 - Exploratory Data Analysis “Random” Model/How you can help Team-FTU.txt 241 Bytes45.9 - Univariate AnalysisGene feature/How you can help Team-FTU.txt 241 Bytes46.1 - BusinessReal world problem Overview/How you can help Team-FTU.txt 241 Bytes46.10 - Data Cleaning Speed/How you can help Team-FTU.txt 241 Bytes46.11 - Data Cleaning Distance/How you can help Team-FTU.txt 241 Bytes46.12 - Data Cleaning Fare/How you can help Team-FTU.txt 241 Bytes46.13 - Data Cleaning Remove all outlierserroneous points/How you can help Team-FTU.txt 241 Bytes46.14 - Data PreparationClusteringSegmentation/How you can help Team-FTU.txt 241 Bytes46.15 - Data PreparationTime binning/How you can help Team-FTU.txt 241 Bytes46.16 - Data PreparationSmoothing time-series data/How you can help Team-FTU.txt 241 Bytes46.17 - Data PreparationSmoothing time-series data cont/How you can help Team-FTU.txt 241 Bytes46.18 - Data Preparation Time series and Fourier transforms/How you can help Team-FTU.txt 241 Bytes46.19 - Ratios and previous-time-bin values/How you can help Team-FTU.txt 241 Bytes46.2 - Objectives and Constraints/How you can help Team-FTU.txt 241 Bytes46.20 - Simple moving average/How you can help Team-FTU.txt 241 Bytes46.21 - Weighted Moving average/How you can help Team-FTU.txt 241 Bytes46.22 - Exponential weighted moving average/How you can help Team-FTU.txt 241 Bytes46.23 - Results/How you can help Team-FTU.txt 241 Bytes46.24 - Regression models Train-Test split & Features/How you can help Team-FTU.txt 241 Bytes46.25 - Linear regression/How you can help Team-FTU.txt 241 Bytes46.26 - Random Forest regression/How you can help Team-FTU.txt 241 Bytes46.27 - Xgboost Regression/How you can help Team-FTU.txt 241 Bytes46.28 - Model comparison/How you can help Team-FTU.txt 241 Bytes46.29 - Assignment/How you can help Team-FTU.txt 241 Bytes46.3 - Mapping to ML problem Data/How you can help Team-FTU.txt 241 Bytes46.4 - Mapping to ML problem dask dataframes/How you can help Team-FTU.txt 241 Bytes46.5 - Mapping to ML problem FieldsFeatures/How you can help Team-FTU.txt 241 Bytes46.6 - Mapping to ML problem Time series forecastingRegression/How you can help Team-FTU.txt 241 Bytes46.7 - Mapping to ML problem Performance metrics/How you can help Team-FTU.txt 241 Bytes46.8 - Data Cleaning Latitude and Longitude data/How you can help Team-FTU.txt 241 Bytes46.9 - Data Cleaning Trip Duration/How you can help Team-FTU.txt 241 Bytes47.1 - History of Neural networks and Deep Learning/How you can help Team-FTU.txt 241 Bytes47.10 - Backpropagation/How you can help Team-FTU.txt 241 Bytes47.11 - Activation functions/How you can help Team-FTU.txt 241 Bytes47.12 - Vanishing Gradient problem/How you can help Team-FTU.txt 241 Bytes47.13 - Bias-Variance tradeoff/How you can help Team-FTU.txt 241 Bytes47.14 - Decision surfaces Playground/How you can help Team-FTU.txt 241 Bytes47.2 - How Biological Neurons work/How you can help Team-FTU.txt 241 Bytes47.3 - Growth of biological neural networks/How you can help Team-FTU.txt 241 Bytes47.4 - Diagrammatic representation Logistic Regression and Perceptron/How you can help Team-FTU.txt 241 Bytes47.5 - Multi-Layered Perceptron (MLP)/How you can help Team-FTU.txt 241 Bytes47.6 - Notation/How you can help Team-FTU.txt 241 Bytes47.7 - Training a single-neuron model/How you can help Team-FTU.txt 241 Bytes47.8 - Training an MLP Chain Rule/How you can help Team-FTU.txt 241 Bytes47.9 - Training an MLPMemoization/How you can help Team-FTU.txt 241 Bytes48.1 - Deep Multi-layer perceptrons1980s to 2010s/How you can help Team-FTU.txt 241 Bytes48.10 - Nesterov Accelerated Gradient (NAG)/How you can help Team-FTU.txt 241 Bytes48.11 - OptimizersAdaGrad/How you can help Team-FTU.txt 241 Bytes48.12 - Optimizers Adadelta andRMSProp/How you can help Team-FTU.txt 241 Bytes48.13 - Adam/How you can help Team-FTU.txt 241 Bytes48.14 - Which algorithm to choose when/How you can help Team-FTU.txt 241 Bytes48.15 - Gradient Checking and clipping/How you can help Team-FTU.txt 241 Bytes48.16 - Softmax and Cross-entropy for multi-class classification/How you can help Team-FTU.txt 241 Bytes48.17 - How to train a Deep MLP/How you can help Team-FTU.txt 241 Bytes48.18 - Auto Encoders/How you can help Team-FTU.txt 241 Bytes48.19 - Word2Vec CBOW/How you can help Team-FTU.txt 241 Bytes48.2 - Dropout layers & Regularization/How you can help Team-FTU.txt 241 Bytes48.20 - Word2Vec Skip-gram/How you can help Team-FTU.txt 241 Bytes48.21 - Word2Vec Algorithmic Optimizations/How you can help Team-FTU.txt 241 Bytes48.3 - Rectified Linear Units (ReLU)/How you can help Team-FTU.txt 241 Bytes48.4 - Weight initialization/How you can help Team-FTU.txt 241 Bytes48.5 - Batch Normalization/How you can help Team-FTU.txt 241 Bytes48.6 - OptimizersHill-descent analogy in 2D/How you can help Team-FTU.txt 241 Bytes48.7 - OptimizersHill descent in 3D and contours/How you can help Team-FTU.txt 241 Bytes48.8 - SGD Recap/How you can help Team-FTU.txt 241 Bytes48.9 - Batch SGD with momentum/How you can help Team-FTU.txt 241 Bytes49.1 - Tensorflow and Keras overview/How you can help Team-FTU.txt 241 Bytes49.10 - Model 3 Batch Normalization/How you can help Team-FTU.txt 241 Bytes49.11 - Model 4 Dropout/How you can help Team-FTU.txt 241 Bytes49.12 - MNIST classification in Keras/How you can help Team-FTU.txt 241 Bytes49.13 - Hyperparameter tuning in Keras/How you can help Team-FTU.txt 241 Bytes49.14 - Exercise Try different MLP architectures on MNIST dataset/How you can help Team-FTU.txt 241 Bytes49.2 - GPU vs CPU for Deep Learning/How you can help Team-FTU.txt 241 Bytes49.3 - Google Colaboratory/How you can help Team-FTU.txt 241 Bytes49.4 - Install TensorFlow/How you can help Team-FTU.txt 241 Bytes49.5 - Online documentation and tutorials/How you can help Team-FTU.txt 241 Bytes49.6 - Softmax Classifier on MNIST dataset/How you can help Team-FTU.txt 241 Bytes49.7 - MLP Initialization/How you can help Team-FTU.txt 241 Bytes49.8 - Model 1 Sigmoid activation/How you can help Team-FTU.txt 241 Bytes49.9 - Model 2 ReLU activation/How you can help Team-FTU.txt 241 Bytes5.1 - Numpy Introduction/How you can help Team-FTU.txt 241 Bytes5.2 - Numerical operations on Numpy/How you can help Team-FTU.txt 241 Bytes50.1 - Biological inspiration Visual Cortex/How you can help Team-FTU.txt 241 Bytes50.10 - Data Augmentation/How you can help Team-FTU.txt 241 Bytes50.11 - Convolution Layers in Keras/How you can help Team-FTU.txt 241 Bytes50.12 - AlexNet/How you can help Team-FTU.txt 241 Bytes50.13 - VGGNet/How you can help Team-FTU.txt 241 Bytes50.14 - Residual Network/How you can help Team-FTU.txt 241 Bytes50.15 - Inception Network/How you can help Team-FTU.txt 241 Bytes50.16 - What is Transfer learning/How you can help Team-FTU.txt 241 Bytes50.17 - Code example Cats vs Dogs/How you can help Team-FTU.txt 241 Bytes50.18 - Code Example MNIST dataset/How you can help Team-FTU.txt 241 Bytes50.19 - Assignment Try various CNN networks on MNIST dataset#/How you can help Team-FTU.txt 241 Bytes50.2 - ConvolutionEdge Detection on images/How you can help Team-FTU.txt 241 Bytes50.3 - ConvolutionPadding and strides/How you can help Team-FTU.txt 241 Bytes50.4 - Convolution over RGB images/How you can help Team-FTU.txt 241 Bytes50.5 - Convolutional layer/How you can help Team-FTU.txt 241 Bytes50.6 - Max-pooling/How you can help Team-FTU.txt 241 Bytes50.7 - CNN Training Optimization/How you can help Team-FTU.txt 241 Bytes50.8 - Example CNN LeNet [1998]/How you can help Team-FTU.txt 241 Bytes50.9 - ImageNet dataset/How you can help Team-FTU.txt 241 Bytes51.1 - Why RNNs/How you can help Team-FTU.txt 241 Bytes51.10 - Code example IMDB Sentiment classification/How you can help Team-FTU.txt 241 Bytes51.11 - Exercise Amazon Fine Food reviews LSTM model/How you can help Team-FTU.txt 241 Bytes51.2 - Recurrent Neural Network/How you can help Team-FTU.txt 241 Bytes51.3 - Training RNNs Backprop/How you can help Team-FTU.txt 241 Bytes51.4 - Types of RNNs/How you can help Team-FTU.txt 241 Bytes51.5 - Need for LSTMGRU/How you can help Team-FTU.txt 241 Bytes51.6 - LSTM/How you can help Team-FTU.txt 241 Bytes51.7 - GRUs/How you can help Team-FTU.txt 241 Bytes51.8 - Deep RNN/How you can help Team-FTU.txt 241 Bytes51.9 - Bidirectional RNN/How you can help Team-FTU.txt 241 Bytes52.1 - Questions and Answers/How you can help Team-FTU.txt 241 Bytes53.1 - Self Driving Car Problem definition/How you can help Team-FTU.txt 241 Bytes53.10 - NVIDIA’s end to end CNN model/How you can help Team-FTU.txt 241 Bytes53.11 - Train the model/How you can help Team-FTU.txt 241 Bytes53.12 - Test and visualize the output/How you can help Team-FTU.txt 241 Bytes53.13 - Extensions/How you can help Team-FTU.txt 241 Bytes53.14 - Assignment/How you can help Team-FTU.txt 241 Bytes53.2 - Datasets#/How you can help Team-FTU.txt 241 Bytes53.2 - Datasets/How you can help Team-FTU.txt 241 Bytes53.3 - Data understanding & Analysis Files and folders/How you can help Team-FTU.txt 241 Bytes53.4 - Dash-cam images and steering angles/How you can help Team-FTU.txt 241 Bytes53.5 - Split the dataset Train vs Test/How you can help Team-FTU.txt 241 Bytes53.6 - EDA Steering angles/How you can help Team-FTU.txt 241 Bytes53.7 - Mean Baseline model simple/How you can help Team-FTU.txt 241 Bytes53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/How you can help Team-FTU.txt 241 Bytes53.9 - Batch load the dataset/How you can help Team-FTU.txt 241 Bytes54.1 - Real-world problem/How you can help Team-FTU.txt 241 Bytes54.10 - MIDI music generation/How you can help Team-FTU.txt 241 Bytes54.11 - Survey blog/How you can help Team-FTU.txt 241 Bytes54.2 - Music representation/How you can help Team-FTU.txt 241 Bytes54.3 - Char-RNN with abc-notation Char-RNN model/How you can help Team-FTU.txt 241 Bytes54.4 - Char-RNN with abc-notation Data preparation/How you can help Team-FTU.txt 241 Bytes54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/How you can help Team-FTU.txt 241 Bytes54.6 - Char-RNN with abc-notation State full RNN/How you can help Team-FTU.txt 241 Bytes54.7 - Char-RNN with abc-notation Model architecture,Model training/How you can help Team-FTU.txt 241 Bytes54.8 - Char-RNN with abc-notation Music generation/How you can help Team-FTU.txt 241 Bytes54.9 - Char-RNN with abc-notation Generate tabla music/How you can help Team-FTU.txt 241 Bytes55.1 - Human Activity Recognition Problem definition/How you can help Team-FTU.txt 241 Bytes55.2 - Dataset understanding/How you can help Team-FTU.txt 241 Bytes55.3 - Data cleaning & preprocessing/How you can help Team-FTU.txt 241 Bytes55.4 - EDAUnivariate analysis/How you can help Team-FTU.txt 241 Bytes55.5 - EDAData visualization using t-SNE/How you can help Team-FTU.txt 241 Bytes55.6 - Classical ML models/How you can help Team-FTU.txt 241 Bytes55.7 - Deep-learning Model/How you can help Team-FTU.txt 241 Bytes55.8 - Exercise Build deeper LSTM models and hyper-param tune them/How you can help Team-FTU.txt 241 Bytes56.1 - Problem definition/How you can help Team-FTU.txt 241 Bytes56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/How you can help Team-FTU.txt 241 Bytes56.11 - PageRank/How you can help Team-FTU.txt 241 Bytes56.12 - Shortest Path/How you can help Team-FTU.txt 241 Bytes56.13 - Connected-components/How you can help Team-FTU.txt 241 Bytes56.14 - Adar Index/How you can help Team-FTU.txt 241 Bytes56.15 - Kartz Centrality/How you can help Team-FTU.txt 241 Bytes56.16 - HITS Score/How you can help Team-FTU.txt 241 Bytes56.17 - SVD/How you can help Team-FTU.txt 241 Bytes56.18 - Weight features/How you can help Team-FTU.txt 241 Bytes56.19 - Modeling/How you can help Team-FTU.txt 241 Bytes56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/How you can help Team-FTU.txt 241 Bytes56.3 - Data format & Limitations/How you can help Team-FTU.txt 241 Bytes56.4 - Mapping to a supervised classification problem/How you can help Team-FTU.txt 241 Bytes56.5 - Business constraints & Metrics/How you can help Team-FTU.txt 241 Bytes56.6 - EDABasic Stats/How you can help Team-FTU.txt 241 Bytes56.7 - EDAFollower and following stats/How you can help Team-FTU.txt 241 Bytes56.8 - EDABinary Classification Task/How you can help Team-FTU.txt 241 Bytes56.9 - EDATrain and test split/How you can help Team-FTU.txt 241 Bytes57.1 - Introduction to Databases/How you can help Team-FTU.txt 241 Bytes57.10 - ORDER BY/How you can help Team-FTU.txt 241 Bytes57.11 - DISTINCT/How you can help Team-FTU.txt 241 Bytes57.12 - WHERE, Comparison operators, NULL/How you can help Team-FTU.txt 241 Bytes57.13 - Logical Operators/How you can help Team-FTU.txt 241 Bytes57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/How you can help Team-FTU.txt 241 Bytes57.15 - GROUP BY/How you can help Team-FTU.txt 241 Bytes57.16 - HAVING/How you can help Team-FTU.txt 241 Bytes57.17 - Order of keywords#/How you can help Team-FTU.txt 241 Bytes57.18 - Join and Natural Join/How you can help Team-FTU.txt 241 Bytes57.19 - Inner, Left, Right and Outer joins/How you can help Team-FTU.txt 241 Bytes57.2 - Why SQL/How you can help Team-FTU.txt 241 Bytes57.20 - Sub QueriesNested QueriesInner Queries/How you can help Team-FTU.txt 241 Bytes57.21 - DMLINSERT/How you can help Team-FTU.txt 241 Bytes57.22 - DMLUPDATE , DELETE/How you can help Team-FTU.txt 241 Bytes57.23 - DDLCREATE TABLE/How you can help Team-FTU.txt 241 Bytes57.24 - DDLALTER ADD, MODIFY, DROP/How you can help Team-FTU.txt 241 Bytes57.25 - DDLDROP TABLE, TRUNCATE, DELETE/How you can help Team-FTU.txt 241 Bytes57.26 - Data Control Language GRANT, REVOKE/How you can help Team-FTU.txt 241 Bytes57.27 - Learning resources/How you can help Team-FTU.txt 241 Bytes57.3 - Execution of an SQL statement/How you can help Team-FTU.txt 241 Bytes57.4 - IMDB dataset/How you can help Team-FTU.txt 241 Bytes57.5 - Installing MySQL/How you can help Team-FTU.txt 241 Bytes57.6 - Load IMDB data/How you can help Team-FTU.txt 241 Bytes57.7 - USE, DESCRIBE, SHOW TABLES/How you can help Team-FTU.txt 241 Bytes57.8 - SELECT/How you can help Team-FTU.txt 241 Bytes57.9 - LIMIT, OFFSET/How you can help Team-FTU.txt 241 Bytes58.1 - AD-Click Predicition/How you can help Team-FTU.txt 241 Bytes59.1 - Revision Questions/How you can help Team-FTU.txt 241 Bytes59.2 - Questions/How you can help Team-FTU.txt 241 Bytes59.3 - External resources for Interview Questions/How you can help Team-FTU.txt 241 Bytes6.1 - Getting started with Matplotlib/How you can help Team-FTU.txt 241 Bytes7.1 - Getting started with pandas/How you can help Team-FTU.txt 241 Bytes7.2 - Data Frame Basics/How you can help Team-FTU.txt 241 Bytes7.3 - Key Operations on Data Frames/How you can help Team-FTU.txt 241 Bytes8.1 - Space and Time Complexity Find largest number in a list/How you can help Team-FTU.txt 241 Bytes8.2 - Binary search/How you can help Team-FTU.txt 241 Bytes8.3 - Find elements common in two lists/How you can help Team-FTU.txt 241 Bytes8.4 - Find elements common in two lists using a HashtableDict/How you can help Team-FTU.txt 241 Bytes9.1 - Introduction to IRIS dataset and 2D scatter plot/How you can help Team-FTU.txt 241 Bytes9.10 - Percentiles and Quantiles/How you can help Team-FTU.txt 241 Bytes9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/How you can help Team-FTU.txt 241 Bytes9.12 - Box-plot with Whiskers/How you can help Team-FTU.txt 241 Bytes9.13 - Violin Plots/How you can help Team-FTU.txt 241 Bytes9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/How you can help Team-FTU.txt 241 Bytes9.15 - Multivariate Probability Density, Contour Plot/How you can help Team-FTU.txt 241 Bytes9.16 - Exercise Perform EDA on Haberman dataset/How you can help Team-FTU.txt 241 Bytes9.2 - 3D scatter plot/How you can help Team-FTU.txt 241 Bytes9.3 - Pair plots/How you can help Team-FTU.txt 241 Bytes9.4 - Limitations of Pair Plots/How you can help Team-FTU.txt 241 Bytes9.5 - Histogram and Introduction to PDF(Probability Density Function)/How you can help Team-FTU.txt 241 Bytes9.6 - Univariate Analysis using PDF/How you can help Team-FTU.txt 241 Bytes9.7 - CDF(Cumulative Distribution Function)/How you can help Team-FTU.txt 241 Bytes9.8 - Mean, Variance and Standard Deviation/How you can help Team-FTU.txt 241 Bytes9.9 - Median/How you can help Team-FTU.txt 241 Bytes1.1 - How to Learn from Appliedaicourse/[FreeCoursesOnline.Me].url 133 Bytes1.2 - How the Job Guarantee program works/[FreeCoursesOnline.Me].url 133 Bytes10.1 - Why learn it/[FreeCoursesOnline.Me].url 133 Bytes10.10 - Hyper Cube,Hyper Cuboid/[FreeCoursesOnline.Me].url 133 Bytes10.11 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/[FreeCoursesOnline.Me].url 133 Bytes10.3 - Dot Product and Angle between 2 Vectors/[FreeCoursesOnline.Me].url 133 Bytes10.4 - Projection and Unit Vector/[FreeCoursesOnline.Me].url 133 Bytes10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/[FreeCoursesOnline.Me].url 133 Bytes10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/[FreeCoursesOnline.Me].url 133 Bytes10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/[FreeCoursesOnline.Me].url 133 Bytes10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/[FreeCoursesOnline.Me].url 133 Bytes10.9 - Square ,Rectangle/[FreeCoursesOnline.Me].url 133 Bytes11.1 - Introduction to Probability and Statistics/[FreeCoursesOnline.Me].url 133 Bytes11.10 - How distributions are used/[FreeCoursesOnline.Me].url 133 Bytes11.11 - Chebyshev’s inequality/[FreeCoursesOnline.Me].url 133 Bytes11.12 - Discrete and Continuous Uniform distributions/[FreeCoursesOnline.Me].url 133 Bytes11.13 - How to randomly sample data points (Uniform Distribution)/[FreeCoursesOnline.Me].url 133 Bytes11.14 - Bernoulli and Binomial Distribution/[FreeCoursesOnline.Me].url 133 Bytes11.15 - Log Normal Distribution/[FreeCoursesOnline.Me].url 133 Bytes11.16 - Power law distribution/[FreeCoursesOnline.Me].url 133 Bytes11.17 - Box cox transform/[FreeCoursesOnline.Me].url 133 Bytes11.18 - Applications of non-gaussian distributions/[FreeCoursesOnline.Me].url 133 Bytes11.19 - Co-variance/[FreeCoursesOnline.Me].url 133 Bytes11.2 - Population and Sample/[FreeCoursesOnline.Me].url 133 Bytes11.20 - Pearson Correlation Coefficient/[FreeCoursesOnline.Me].url 133 Bytes11.21 - Spearman Rank Correlation Coefficient/[FreeCoursesOnline.Me].url 133 Bytes11.22 - Correlation vs Causation/[FreeCoursesOnline.Me].url 133 Bytes11.23 - How to use correlations/[FreeCoursesOnline.Me].url 133 Bytes11.24 - Confidence interval (C.I) Introduction/[FreeCoursesOnline.Me].url 133 Bytes11.25 - Computing confidence interval given the underlying distribution/[FreeCoursesOnline.Me].url 133 Bytes11.26 - C.I for mean of a normal random variable/[FreeCoursesOnline.Me].url 133 Bytes11.27 - Confidence interval using bootstrapping/[FreeCoursesOnline.Me].url 133 Bytes11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/[FreeCoursesOnline.Me].url 133 Bytes11.29 - Hypothesis Testing Intution with coin toss example/[FreeCoursesOnline.Me].url 133 Bytes11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/[FreeCoursesOnline.Me].url 133 Bytes11.30 - Resampling and permutation test/[FreeCoursesOnline.Me].url 133 Bytes11.31 - K-S Test for similarity of two distributions/[FreeCoursesOnline.Me].url 133 Bytes11.32 - Code Snippet K-S Test/[FreeCoursesOnline.Me].url 133 Bytes11.33 - Hypothesis testing another example/[FreeCoursesOnline.Me].url 133 Bytes11.34 - Resampling and Permutation test another example/[FreeCoursesOnline.Me].url 133 Bytes11.35 - How to use hypothesis testing/[FreeCoursesOnline.Me].url 133 Bytes11.36 - Proportional Sampling/[FreeCoursesOnline.Me].url 133 Bytes11.37 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/[FreeCoursesOnline.Me].url 133 Bytes11.5 - Symmetric distribution, Skewness and Kurtosis/[FreeCoursesOnline.Me].url 133 Bytes11.6 - Standard normal variate (Z) and standardization/[FreeCoursesOnline.Me].url 133 Bytes11.7 - Kernel density estimation/[FreeCoursesOnline.Me].url 133 Bytes11.8 - Sampling distribution & Central Limit theorem/[FreeCoursesOnline.Me].url 133 Bytes11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/[FreeCoursesOnline.Me].url 133 Bytes12.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes13.1 - What is Dimensionality reduction/[FreeCoursesOnline.Me].url 133 Bytes13.10 - Code to Load MNIST Data Set/[FreeCoursesOnline.Me].url 133 Bytes13.2 - Row Vector and Column Vector/[FreeCoursesOnline.Me].url 133 Bytes13.3 - How to represent a data set/[FreeCoursesOnline.Me].url 133 Bytes13.4 - How to represent a dataset as a Matrix/[FreeCoursesOnline.Me].url 133 Bytes13.5 - Data Preprocessing Feature Normalisation/[FreeCoursesOnline.Me].url 133 Bytes13.6 - Mean of a data matrix/[FreeCoursesOnline.Me].url 133 Bytes13.7 - Data Preprocessing Column Standardization/[FreeCoursesOnline.Me].url 133 Bytes13.8 - Co-variance of a Data Matrix/[FreeCoursesOnline.Me].url 133 Bytes13.9 - MNIST dataset (784 dimensional)/[FreeCoursesOnline.Me].url 133 Bytes14.1 - Why learn PCA/[FreeCoursesOnline.Me].url 133 Bytes14.10 - PCA for dimensionality reduction (not-visualization)/[FreeCoursesOnline.Me].url 133 Bytes14.2 - Geometric intuition of PCA/[FreeCoursesOnline.Me].url 133 Bytes14.3 - Mathematical objective function of PCA/[FreeCoursesOnline.Me].url 133 Bytes14.4 - Alternative formulation of PCA Distance minimization/[FreeCoursesOnline.Me].url 133 Bytes14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/[FreeCoursesOnline.Me].url 133 Bytes14.6 - PCA for Dimensionality Reduction and Visualization/[FreeCoursesOnline.Me].url 133 Bytes14.7 - Visualize MNIST dataset/[FreeCoursesOnline.Me].url 133 Bytes14.8 - Limitations of PCA/[FreeCoursesOnline.Me].url 133 Bytes14.9 - PCA Code example/[FreeCoursesOnline.Me].url 133 Bytes15.1 - What is t-SNE/[FreeCoursesOnline.Me].url 133 Bytes15.2 - Neighborhood of a point, Embedding/[FreeCoursesOnline.Me].url 133 Bytes15.3 - Geometric intuition of t-SNE/[FreeCoursesOnline.Me].url 133 Bytes15.4 - Crowding Problem/[FreeCoursesOnline.Me].url 133 Bytes15.5 - How to apply t-SNE and interpret its output/[FreeCoursesOnline.Me].url 133 Bytes15.6 - t-SNE on MNIST/[FreeCoursesOnline.Me].url 133 Bytes15.7 - Code example of t-SNE/[FreeCoursesOnline.Me].url 133 Bytes15.8 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes16.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes17.1 - Dataset overview Amazon Fine Food reviews(EDA)/[FreeCoursesOnline.Me].url 133 Bytes17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/[FreeCoursesOnline.Me].url 133 Bytes17.11 - Bag of Words( Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.12 - Text Preprocessing( Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.13 - Bi-Grams and n-grams (Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.14 - TF-IDF (Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.15 - Word2Vec (Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/[FreeCoursesOnline.Me].url 133 Bytes17.17 - Assignment-2 Apply t-SNE/[FreeCoursesOnline.Me].url 133 Bytes17.2 - Data Cleaning Deduplication/[FreeCoursesOnline.Me].url 133 Bytes17.3 - Why convert text to a vector/[FreeCoursesOnline.Me].url 133 Bytes17.4 - Bag of Words (BoW)/[FreeCoursesOnline.Me].url 133 Bytes17.5 - Text Preprocessing Stemming/[FreeCoursesOnline.Me].url 133 Bytes17.6 - uni-gram, bi-gram, n-grams/[FreeCoursesOnline.Me].url 133 Bytes17.7 - tf-idf (term frequency- inverse document frequency)/[FreeCoursesOnline.Me].url 133 Bytes17.8 - Why use log in IDF/[FreeCoursesOnline.Me].url 133 Bytes17.9 - Word2Vec/[FreeCoursesOnline.Me].url 133 Bytes18.1 - How “Classification” works/[FreeCoursesOnline.Me].url 133 Bytes18.10 - KNN Limitations/[FreeCoursesOnline.Me].url 133 Bytes18.11 - Decision surface for K-NN as K changes/[FreeCoursesOnline.Me].url 133 Bytes18.12 - Overfitting and Underfitting/[FreeCoursesOnline.Me].url 133 Bytes18.13 - Need for Cross validation/[FreeCoursesOnline.Me].url 133 Bytes18.14 - K-fold cross validation/[FreeCoursesOnline.Me].url 133 Bytes18.15 - Visualizing train, validation and test datasets/[FreeCoursesOnline.Me].url 133 Bytes18.16 - How to determine overfitting and underfitting/[FreeCoursesOnline.Me].url 133 Bytes18.17 - Time based splitting/[FreeCoursesOnline.Me].url 133 Bytes18.18 - k-NN for regression/[FreeCoursesOnline.Me].url 133 Bytes18.19 - Weighted k-NN/[FreeCoursesOnline.Me].url 133 Bytes18.2 - Data matrix notation/[FreeCoursesOnline.Me].url 133 Bytes18.20 - Voronoi diagram/[FreeCoursesOnline.Me].url 133 Bytes18.21 - Binary search tree/[FreeCoursesOnline.Me].url 133 Bytes18.22 - How to build a kd-tree/[FreeCoursesOnline.Me].url 133 Bytes18.23 - Find nearest neighbours using kd-tree/[FreeCoursesOnline.Me].url 133 Bytes18.24 - Limitations of Kd tree/[FreeCoursesOnline.Me].url 133 Bytes18.25 - Extensions/[FreeCoursesOnline.Me].url 133 Bytes18.26 - Hashing vs LSH/[FreeCoursesOnline.Me].url 133 Bytes18.27 - LSH for cosine similarity/[FreeCoursesOnline.Me].url 133 Bytes18.28 - LSH for euclidean distance/[FreeCoursesOnline.Me].url 133 Bytes18.29 - Probabilistic class label/[FreeCoursesOnline.Me].url 133 Bytes18.3 - Classification vs Regression (examples)/[FreeCoursesOnline.Me].url 133 Bytes18.30 - Code SampleDecision boundary/[FreeCoursesOnline.Me].url 133 Bytes18.31 - Code SampleCross Validation/[FreeCoursesOnline.Me].url 133 Bytes18.32 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes18.4 - K-Nearest Neighbours Geometric intuition with a toy example/[FreeCoursesOnline.Me].url 133 Bytes18.5 - Failure cases of KNN/[FreeCoursesOnline.Me].url 133 Bytes18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/[FreeCoursesOnline.Me].url 133 Bytes18.7 - Cosine Distance & Cosine Similarity/[FreeCoursesOnline.Me].url 133 Bytes18.8 - How to measure the effectiveness of k-NN/[FreeCoursesOnline.Me].url 133 Bytes18.9 - TestEvaluation time and space complexity/[FreeCoursesOnline.Me].url 133 Bytes19.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes2.1 - Python, Anaconda and relevant packages installations/[FreeCoursesOnline.Me].url 133 Bytes2.10 - Control flow for loop/[FreeCoursesOnline.Me].url 133 Bytes2.11 - Control flow break and continue/[FreeCoursesOnline.Me].url 133 Bytes2.2 - Why learn Python/[FreeCoursesOnline.Me].url 133 Bytes2.3 - Keywords and identifiers/[FreeCoursesOnline.Me].url 133 Bytes2.4 - comments, indentation and statements/[FreeCoursesOnline.Me].url 133 Bytes2.5 - Variables and data types in Python/[FreeCoursesOnline.Me].url 133 Bytes2.6 - Standard Input and Output/[FreeCoursesOnline.Me].url 133 Bytes2.7 - Operators/[FreeCoursesOnline.Me].url 133 Bytes2.8 - Control flow if else/[FreeCoursesOnline.Me].url 133 Bytes2.9 - Control flow while loop/[FreeCoursesOnline.Me].url 133 Bytes20.1 - Introduction/[FreeCoursesOnline.Me].url 133 Bytes20.10 - Local reachability-density(A)/[FreeCoursesOnline.Me].url 133 Bytes20.11 - Local outlier Factor(A)/[FreeCoursesOnline.Me].url 133 Bytes20.12 - Impact of Scale & Column standardization/[FreeCoursesOnline.Me].url 133 Bytes20.13 - Interpretability/[FreeCoursesOnline.Me].url 133 Bytes20.14 - Feature Importance and Forward Feature selection/[FreeCoursesOnline.Me].url 133 Bytes20.15 - Handling categorical and numerical features/[FreeCoursesOnline.Me].url 133 Bytes20.16 - Handling missing values by imputation/[FreeCoursesOnline.Me].url 133 Bytes20.17 - curse of dimensionality/[FreeCoursesOnline.Me].url 133 Bytes20.18 - Bias-Variance tradeoff/[FreeCoursesOnline.Me].url 133 Bytes20.19 - Intuitive understanding of bias-variance/[FreeCoursesOnline.Me].url 133 Bytes20.2 - Imbalanced vs balanced dataset/[FreeCoursesOnline.Me].url 133 Bytes20.20 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes20.21 - best and wrost case of algorithm/[FreeCoursesOnline.Me].url 133 Bytes20.3 - Multi-class classification/[FreeCoursesOnline.Me].url 133 Bytes20.4 - k-NN, given a distance or similarity matrix/[FreeCoursesOnline.Me].url 133 Bytes20.5 - Train and test set differences/[FreeCoursesOnline.Me].url 133 Bytes20.6 - Impact of outliers/[FreeCoursesOnline.Me].url 133 Bytes20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/[FreeCoursesOnline.Me].url 133 Bytes20.8 - k distance/[FreeCoursesOnline.Me].url 133 Bytes20.9 - Reachability-Distance(A,B)/[FreeCoursesOnline.Me].url 133 Bytes21.1 - Accuracy/[FreeCoursesOnline.Me].url 133 Bytes21.10 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes21.2 - Confusion matrix, TPR, FPR, FNR, TNR/[FreeCoursesOnline.Me].url 133 Bytes21.3 - Precision and recall, F1-score/[FreeCoursesOnline.Me].url 133 Bytes21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/[FreeCoursesOnline.Me].url 133 Bytes21.5 - Log-loss/[FreeCoursesOnline.Me].url 133 Bytes21.6 - R-SquaredCoefficient of determination/[FreeCoursesOnline.Me].url 133 Bytes21.7 - Median absolute deviation (MAD)/[FreeCoursesOnline.Me].url 133 Bytes21.8 - Distribution of errors/[FreeCoursesOnline.Me].url 133 Bytes21.9 - Assignment-3 Apply k-Nearest Neighbor/[FreeCoursesOnline.Me].url 133 Bytes22.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes23.1 - Conditional probability/[FreeCoursesOnline.Me].url 133 Bytes23.10 - Bias and Variance tradeoff/[FreeCoursesOnline.Me].url 133 Bytes23.11 - Feature importance and interpretability/[FreeCoursesOnline.Me].url 133 Bytes23.12 - Imbalanced data/[FreeCoursesOnline.Me].url 133 Bytes23.13 - Outliers/[FreeCoursesOnline.Me].url 133 Bytes23.14 - Missing values/[FreeCoursesOnline.Me].url 133 Bytes23.15 - Handling Numerical features (Gaussian NB)/[FreeCoursesOnline.Me].url 133 Bytes23.16 - Multiclass classification/[FreeCoursesOnline.Me].url 133 Bytes23.17 - Similarity or Distance matrix/[FreeCoursesOnline.Me].url 133 Bytes23.18 - Large dimensionality/[FreeCoursesOnline.Me].url 133 Bytes23.19 - Best and worst cases/[FreeCoursesOnline.Me].url 133 Bytes23.2 - Independent vs Mutually exclusive events/[FreeCoursesOnline.Me].url 133 Bytes23.20 - Code example/[FreeCoursesOnline.Me].url 133 Bytes23.21 - Assignment-4 Apply Naive Bayes/[FreeCoursesOnline.Me].url 133 Bytes23.22 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes23.3 - Bayes Theorem with examples/[FreeCoursesOnline.Me].url 133 Bytes23.4 - Exercise problems on Bayes Theorem/[FreeCoursesOnline.Me].url 133 Bytes23.5 - Naive Bayes algorithm/[FreeCoursesOnline.Me].url 133 Bytes23.6 - Toy example Train and test stages/[FreeCoursesOnline.Me].url 133 Bytes23.7 - Naive Bayes on Text data/[FreeCoursesOnline.Me].url 133 Bytes23.8 - LaplaceAdditive Smoothing/[FreeCoursesOnline.Me].url 133 Bytes23.9 - Log-probabilities for numerical stability/[FreeCoursesOnline.Me].url 133 Bytes24.1 - Geometric intuition of Logistic Regression/[FreeCoursesOnline.Me].url 133 Bytes24.10 - Column Standardization/[FreeCoursesOnline.Me].url 133 Bytes24.11 - Feature importance and Model interpretability/[FreeCoursesOnline.Me].url 133 Bytes24.12 - Collinearity of features/[FreeCoursesOnline.Me].url 133 Bytes24.13 - TestRun time space and time complexity/[FreeCoursesOnline.Me].url 133 Bytes24.14 - Real world cases/[FreeCoursesOnline.Me].url 133 Bytes24.15 - Non-linearly separable data & feature engineering/[FreeCoursesOnline.Me].url 133 Bytes24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/[FreeCoursesOnline.Me].url 133 Bytes24.17 - Assignment-5 Apply Logistic Regression/[FreeCoursesOnline.Me].url 133 Bytes24.18 - Extensions to Generalized linear models/[FreeCoursesOnline.Me].url 133 Bytes24.2 - Sigmoid function Squashing/[FreeCoursesOnline.Me].url 133 Bytes24.3 - Mathematical formulation of Objective function/[FreeCoursesOnline.Me].url 133 Bytes24.4 - Weight vector/[FreeCoursesOnline.Me].url 133 Bytes24.5 - L2 Regularization Overfitting and Underfitting/[FreeCoursesOnline.Me].url 133 Bytes24.6 - L1 regularization and sparsity/[FreeCoursesOnline.Me].url 133 Bytes24.7 - Probabilistic Interpretation Gaussian Naive Bayes/[FreeCoursesOnline.Me].url 133 Bytes24.8 - Loss minimization interpretation/[FreeCoursesOnline.Me].url 133 Bytes24.9 - hyperparameters and random search/[FreeCoursesOnline.Me].url 133 Bytes25.1 - Geometric intuition of Linear Regression/[FreeCoursesOnline.Me].url 133 Bytes25.2 - Mathematical formulation/[FreeCoursesOnline.Me].url 133 Bytes25.3 - Real world Cases/[FreeCoursesOnline.Me].url 133 Bytes25.4 - Code sample for Linear Regression/[FreeCoursesOnline.Me].url 133 Bytes26.1 - Differentiation/[FreeCoursesOnline.Me].url 133 Bytes26.10 - Logistic regression formulation revisited/[FreeCoursesOnline.Me].url 133 Bytes26.11 - Why L1 regularization creates sparsity/[FreeCoursesOnline.Me].url 133 Bytes26.12 - Assignment 6 Implement SGD for linear regression/[FreeCoursesOnline.Me].url 133 Bytes26.13 - Revision questions/[FreeCoursesOnline.Me].url 133 Bytes26.2 - Online differentiation tools/[FreeCoursesOnline.Me].url 133 Bytes26.3 - Maxima and Minima/[FreeCoursesOnline.Me].url 133 Bytes26.4 - Vector calculus Grad/[FreeCoursesOnline.Me].url 133 Bytes26.5 - Gradient descent geometric intuition/[FreeCoursesOnline.Me].url 133 Bytes26.6 - Learning rate/[FreeCoursesOnline.Me].url 133 Bytes26.7 - Gradient descent for linear regression/[FreeCoursesOnline.Me].url 133 Bytes26.8 - SGD algorithm/[FreeCoursesOnline.Me].url 133 Bytes26.9 - Constrained Optimization & PCA/[FreeCoursesOnline.Me].url 133 Bytes27.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes28.1 - Geometric Intution/[FreeCoursesOnline.Me].url 133 Bytes28.10 - Train and run time complexities/[FreeCoursesOnline.Me].url 133 Bytes28.11 - nu-SVM control errors and support vectors/[FreeCoursesOnline.Me].url 133 Bytes28.12 - SVM Regression/[FreeCoursesOnline.Me].url 133 Bytes28.13 - Cases/[FreeCoursesOnline.Me].url 133 Bytes28.14 - Code Sample/[FreeCoursesOnline.Me].url 133 Bytes28.15 - Assignment-7 Apply SVM/[FreeCoursesOnline.Me].url 133 Bytes28.16 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes28.2 - Mathematical derivation/[FreeCoursesOnline.Me].url 133 Bytes28.3 - Why we take values +1 and and -1 for Support vector planes/[FreeCoursesOnline.Me].url 133 Bytes28.4 - Loss function (Hinge Loss) based interpretation/[FreeCoursesOnline.Me].url 133 Bytes28.5 - Dual form of SVM formulation/[FreeCoursesOnline.Me].url 133 Bytes28.6 - kernel trick/[FreeCoursesOnline.Me].url 133 Bytes28.7 - Polynomial Kernel/[FreeCoursesOnline.Me].url 133 Bytes28.8 - RBF-Kernel/[FreeCoursesOnline.Me].url 133 Bytes28.9 - Domain specific Kernels/[FreeCoursesOnline.Me].url 133 Bytes29.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes3.1 - Lists/[FreeCoursesOnline.Me].url 133 Bytes3.2 - Tuples part 1/[FreeCoursesOnline.Me].url 133 Bytes3.3 - Tuples part-2/[FreeCoursesOnline.Me].url 133 Bytes3.4 - Sets/[FreeCoursesOnline.Me].url 133 Bytes3.5 - Dictionary/[FreeCoursesOnline.Me].url 133 Bytes3.6 - Strings/[FreeCoursesOnline.Me].url 133 Bytes30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/[FreeCoursesOnline.Me].url 133 Bytes30.10 - Overfitting and Underfitting/[FreeCoursesOnline.Me].url 133 Bytes30.11 - Train and Run time complexity/[FreeCoursesOnline.Me].url 133 Bytes30.12 - Regression using Decision Trees/[FreeCoursesOnline.Me].url 133 Bytes30.13 - Cases/[FreeCoursesOnline.Me].url 133 Bytes30.14 - Code Samples/[FreeCoursesOnline.Me].url 133 Bytes30.15 - Assignment-8 Apply Decision Trees/[FreeCoursesOnline.Me].url 133 Bytes30.16 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes30.2 - Sample Decision tree/[FreeCoursesOnline.Me].url 133 Bytes30.3 - Building a decision TreeEntropy/[FreeCoursesOnline.Me].url 133 Bytes30.4 - Building a decision TreeInformation Gain/[FreeCoursesOnline.Me].url 133 Bytes30.5 - Building a decision Tree Gini Impurity/[FreeCoursesOnline.Me].url 133 Bytes30.6 - Building a decision Tree Constructing a DT/[FreeCoursesOnline.Me].url 133 Bytes30.7 - Building a decision Tree Splitting numerical features/[FreeCoursesOnline.Me].url 133 Bytes30.8 - Feature standardization/[FreeCoursesOnline.Me].url 133 Bytes30.9 - Building a decision TreeCategorical features with many possible values/[FreeCoursesOnline.Me].url 133 Bytes31.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes32.1 - What are ensembles/[FreeCoursesOnline.Me].url 133 Bytes32.10 - Residuals, Loss functions and gradients/[FreeCoursesOnline.Me].url 133 Bytes32.11 - Gradient Boosting/[FreeCoursesOnline.Me].url 133 Bytes32.12 - Regularization by Shrinkage/[FreeCoursesOnline.Me].url 133 Bytes32.13 - Train and Run time complexity/[FreeCoursesOnline.Me].url 133 Bytes32.14 - XGBoost Boosting + Randomization/[FreeCoursesOnline.Me].url 133 Bytes32.15 - AdaBoost geometric intuition/[FreeCoursesOnline.Me].url 133 Bytes32.16 - Stacking models/[FreeCoursesOnline.Me].url 133 Bytes32.17 - Cascading classifiers/[FreeCoursesOnline.Me].url 133 Bytes32.18 - Kaggle competitions vs Real world/[FreeCoursesOnline.Me].url 133 Bytes32.19 - Assignment-9 Apply Random Forests & GBDT/[FreeCoursesOnline.Me].url 133 Bytes32.2 - Bootstrapped Aggregation (Bagging) Intuition/[FreeCoursesOnline.Me].url 133 Bytes32.20 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes32.3 - Random Forest and their construction/[FreeCoursesOnline.Me].url 133 Bytes32.4 - Bias-Variance tradeoff/[FreeCoursesOnline.Me].url 133 Bytes32.5 - Train and run time complexity/[FreeCoursesOnline.Me].url 133 Bytes32.6 - BaggingCode Sample/[FreeCoursesOnline.Me].url 133 Bytes32.7 - Extremely randomized trees/[FreeCoursesOnline.Me].url 133 Bytes32.8 - Random Tree Cases/[FreeCoursesOnline.Me].url 133 Bytes32.9 - Boosting Intuition/[FreeCoursesOnline.Me].url 133 Bytes33.1 - Introduction/[FreeCoursesOnline.Me].url 133 Bytes33.10 - Indicator variables/[FreeCoursesOnline.Me].url 133 Bytes33.11 - Feature binning/[FreeCoursesOnline.Me].url 133 Bytes33.12 - Interaction variables/[FreeCoursesOnline.Me].url 133 Bytes33.13 - Mathematical transforms/[FreeCoursesOnline.Me].url 133 Bytes33.14 - Model specific featurizations/[FreeCoursesOnline.Me].url 133 Bytes33.15 - Feature orthogonality/[FreeCoursesOnline.Me].url 133 Bytes33.16 - Domain specific featurizations/[FreeCoursesOnline.Me].url 133 Bytes33.17 - Feature slicing/[FreeCoursesOnline.Me].url 133 Bytes33.18 - Kaggle Winners solutions/[FreeCoursesOnline.Me].url 133 Bytes33.2 - Moving window for Time Series Data/[FreeCoursesOnline.Me].url 133 Bytes33.3 - Fourier decomposition/[FreeCoursesOnline.Me].url 133 Bytes33.4 - Deep learning features LSTM/[FreeCoursesOnline.Me].url 133 Bytes33.5 - Image histogram/[FreeCoursesOnline.Me].url 133 Bytes33.6 - Keypoints SIFT/[FreeCoursesOnline.Me].url 133 Bytes33.7 - Deep learning features CNN/[FreeCoursesOnline.Me].url 133 Bytes33.8 - Relational data/[FreeCoursesOnline.Me].url 133 Bytes33.9 - Graph data/[FreeCoursesOnline.Me].url 133 Bytes34.1 - Calibration of ModelsNeed for calibration/[FreeCoursesOnline.Me].url 133 Bytes34.10 - AB testing/[FreeCoursesOnline.Me].url 133 Bytes34.11 - Data Science Life cycle/[FreeCoursesOnline.Me].url 133 Bytes34.12 - VC dimension/[FreeCoursesOnline.Me].url 133 Bytes34.2 - Productionization and deployment of Machine Learning Models/[FreeCoursesOnline.Me].url 133 Bytes34.3 - Calibration Plots/[FreeCoursesOnline.Me].url 133 Bytes34.4 - Platt’s CalibrationScaling/[FreeCoursesOnline.Me].url 133 Bytes34.5 - Isotonic Regression/[FreeCoursesOnline.Me].url 133 Bytes34.6 - Code Samples/[FreeCoursesOnline.Me].url 133 Bytes34.7 - Modeling in the presence of outliers RANSAC/[FreeCoursesOnline.Me].url 133 Bytes34.8 - Productionizing models/[FreeCoursesOnline.Me].url 133 Bytes34.9 - Retraining models periodically/[FreeCoursesOnline.Me].url 133 Bytes35.1 - What is Clustering/[FreeCoursesOnline.Me].url 133 Bytes35.10 - K-Medoids/[FreeCoursesOnline.Me].url 133 Bytes35.11 - Determining the right K/[FreeCoursesOnline.Me].url 133 Bytes35.12 - Code Samples/[FreeCoursesOnline.Me].url 133 Bytes35.13 - Time and space complexity/[FreeCoursesOnline.Me].url 133 Bytes35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeCoursesOnline.Me].url 133 Bytes35.2 - Unsupervised learning/[FreeCoursesOnline.Me].url 133 Bytes35.3 - Applications/[FreeCoursesOnline.Me].url 133 Bytes35.4 - Metrics for Clustering/[FreeCoursesOnline.Me].url 133 Bytes35.5 - K-Means Geometric intuition, Centroids/[FreeCoursesOnline.Me].url 133 Bytes35.6 - K-Means Mathematical formulation Objective function/[FreeCoursesOnline.Me].url 133 Bytes35.7 - K-Means Algorithm/[FreeCoursesOnline.Me].url 133 Bytes35.8 - How to initialize K-Means++/[FreeCoursesOnline.Me].url 133 Bytes35.9 - Failure casesLimitations/[FreeCoursesOnline.Me].url 133 Bytes36.1 - Agglomerative & Divisive, Dendrograms/[FreeCoursesOnline.Me].url 133 Bytes36.2 - Agglomerative Clustering/[FreeCoursesOnline.Me].url 133 Bytes36.3 - Proximity methods Advantages and Limitations/[FreeCoursesOnline.Me].url 133 Bytes36.4 - Time and Space Complexity/[FreeCoursesOnline.Me].url 133 Bytes36.5 - Limitations of Hierarchical Clustering/[FreeCoursesOnline.Me].url 133 Bytes36.6 - Code sample/[FreeCoursesOnline.Me].url 133 Bytes36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeCoursesOnline.Me].url 133 Bytes37.1 - Density based clustering/[FreeCoursesOnline.Me].url 133 Bytes37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeCoursesOnline.Me].url 133 Bytes37.11 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes37.2 - MinPts and Eps Density/[FreeCoursesOnline.Me].url 133 Bytes37.3 - Core, Border and Noise points/[FreeCoursesOnline.Me].url 133 Bytes37.4 - Density edge and Density connected points/[FreeCoursesOnline.Me].url 133 Bytes37.5 - DBSCAN Algorithm/[FreeCoursesOnline.Me].url 133 Bytes37.6 - Hyper Parameters MinPts and Eps/[FreeCoursesOnline.Me].url 133 Bytes37.7 - Advantages and Limitations of DBSCAN/[FreeCoursesOnline.Me].url 133 Bytes37.8 - Time and Space Complexity/[FreeCoursesOnline.Me].url 133 Bytes37.9 - Code samples/[FreeCoursesOnline.Me].url 133 Bytes38.1 - Problem formulation Movie reviews/[FreeCoursesOnline.Me].url 133 Bytes38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/[FreeCoursesOnline.Me].url 133 Bytes38.11 - Cold Start problem/[FreeCoursesOnline.Me].url 133 Bytes38.12 - Word vectors as MF/[FreeCoursesOnline.Me].url 133 Bytes38.13 - Eigen-Faces/[FreeCoursesOnline.Me].url 133 Bytes38.14 - Code example/[FreeCoursesOnline.Me].url 133 Bytes38.15 - Assignment-11 Apply Truncated SVD/[FreeCoursesOnline.Me].url 133 Bytes38.16 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes38.2 - Content based vs Collaborative Filtering/[FreeCoursesOnline.Me].url 133 Bytes38.3 - Similarity based Algorithms/[FreeCoursesOnline.Me].url 133 Bytes38.4 - Matrix Factorization PCA, SVD/[FreeCoursesOnline.Me].url 133 Bytes38.5 - Matrix Factorization NMF/[FreeCoursesOnline.Me].url 133 Bytes38.6 - Matrix Factorization for Collaborative filtering/[FreeCoursesOnline.Me].url 133 Bytes38.7 - Matrix Factorization for feature engineering/[FreeCoursesOnline.Me].url 133 Bytes38.8 - Clustering as MF/[FreeCoursesOnline.Me].url 133 Bytes38.9 - Hyperparameter tuning/[FreeCoursesOnline.Me].url 133 Bytes39.1 - Questions & Answers/[FreeCoursesOnline.Me].url 133 Bytes4.1 - Introduction/[FreeCoursesOnline.Me].url 133 Bytes4.10 - Debugging Python/[FreeCoursesOnline.Me].url 133 Bytes4.2 - Types of functions/[FreeCoursesOnline.Me].url 133 Bytes4.3 - Function arguments/[FreeCoursesOnline.Me].url 133 Bytes4.4 - Recursive functions/[FreeCoursesOnline.Me].url 133 Bytes4.5 - Lambda functions/[FreeCoursesOnline.Me].url 133 Bytes4.6 - Modules/[FreeCoursesOnline.Me].url 133 Bytes4.7 - Packages/[FreeCoursesOnline.Me].url 133 Bytes4.8 - File Handling/[FreeCoursesOnline.Me].url 133 Bytes4.9 - Exception Handling/[FreeCoursesOnline.Me].url 133 Bytes40.1 - BusinessReal world problem/[FreeCoursesOnline.Me].url 133 Bytes40.10 - Data Modeling Multi label Classification/[FreeCoursesOnline.Me].url 133 Bytes40.11 - Data preparation/[FreeCoursesOnline.Me].url 133 Bytes40.12 - Train-Test Split/[FreeCoursesOnline.Me].url 133 Bytes40.13 - Featurization/[FreeCoursesOnline.Me].url 133 Bytes40.14 - Logistic regression One VS Rest/[FreeCoursesOnline.Me].url 133 Bytes40.15 - Sampling data and tags+Weighted models/[FreeCoursesOnline.Me].url 133 Bytes40.16 - Logistic regression revisited/[FreeCoursesOnline.Me].url 133 Bytes40.17 - Why not use advanced techniques/[FreeCoursesOnline.Me].url 133 Bytes40.18 - Assignments/[FreeCoursesOnline.Me].url 133 Bytes40.2 - Business objectives and constraints/[FreeCoursesOnline.Me].url 133 Bytes40.3 - Mapping to an ML problem Data overview/[FreeCoursesOnline.Me].url 133 Bytes40.4 - Mapping to an ML problemML problem formulation/[FreeCoursesOnline.Me].url 133 Bytes40.5 - Mapping to an ML problemPerformance metrics/[FreeCoursesOnline.Me].url 133 Bytes40.6 - Hamming loss/[FreeCoursesOnline.Me].url 133 Bytes40.7 - EDAData Loading/[FreeCoursesOnline.Me].url 133 Bytes40.8 - EDAAnalysis of tags/[FreeCoursesOnline.Me].url 133 Bytes40.9 - EDAData Preprocessing/[FreeCoursesOnline.Me].url 133 Bytes41.1 - BusinessReal world problem Problem definition/[FreeCoursesOnline.Me].url 133 Bytes41.10 - EDA Feature analysis/[FreeCoursesOnline.Me].url 133 Bytes41.11 - EDA Data Visualization T-SNE/[FreeCoursesOnline.Me].url 133 Bytes41.12 - EDA TF-IDF weighted Word2Vec featurization/[FreeCoursesOnline.Me].url 133 Bytes41.13 - ML Models Loading Data/[FreeCoursesOnline.Me].url 133 Bytes41.14 - ML Models Random Model/[FreeCoursesOnline.Me].url 133 Bytes41.15 - ML Models Logistic Regression and Linear SVM/[FreeCoursesOnline.Me].url 133 Bytes41.16 - ML Models XGBoost/[FreeCoursesOnline.Me].url 133 Bytes41.17 - Assignments/[FreeCoursesOnline.Me].url 133 Bytes41.2 - Business objectives and constraints/[FreeCoursesOnline.Me].url 133 Bytes41.3 - Mapping to an ML problem Data overview/[FreeCoursesOnline.Me].url 133 Bytes41.4 - Mapping to an ML problem ML problem and performance metric/[FreeCoursesOnline.Me].url 133 Bytes41.5 - Mapping to an ML problem Train-test split/[FreeCoursesOnline.Me].url 133 Bytes41.6 - EDA Basic Statistics/[FreeCoursesOnline.Me].url 133 Bytes41.7 - EDA Basic Feature Extraction/[FreeCoursesOnline.Me].url 133 Bytes41.8 - EDA Text Preprocessing/[FreeCoursesOnline.Me].url 133 Bytes41.9 - EDA Advanced Feature Extraction/[FreeCoursesOnline.Me].url 133 Bytes42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/[FreeCoursesOnline.Me].url 133 Bytes42.10 - Text Pre-Processing Tokenization and Stop-word removal/[FreeCoursesOnline.Me].url 133 Bytes42.11 - Stemming/[FreeCoursesOnline.Me].url 133 Bytes42.12 - Text based product similarity Converting text to an n-D vector bag of words/[FreeCoursesOnline.Me].url 133 Bytes42.13 - Code for bag of words based product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.14 - TF-IDF featurizing text based on word-importance/[FreeCoursesOnline.Me].url 133 Bytes42.15 - Code for TF-IDF based product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.16 - Code for IDF based product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/[FreeCoursesOnline.Me].url 133 Bytes42.18 - Code for Average Word2Vec product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.19 - TF-IDF weighted Word2Vec/[FreeCoursesOnline.Me].url 133 Bytes42.2 - Plan of action/[FreeCoursesOnline.Me].url 133 Bytes42.20 - Code for IDF weighted Word2Vec product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.21 - Weighted similarity using brand and color/[FreeCoursesOnline.Me].url 133 Bytes42.22 - Code for weighted similarity/[FreeCoursesOnline.Me].url 133 Bytes42.23 - Building a real world solution/[FreeCoursesOnline.Me].url 133 Bytes42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/[FreeCoursesOnline.Me].url 133 Bytes42.25 - Using Keras + Tensorflow to extract features/[FreeCoursesOnline.Me].url 133 Bytes42.26 - Visual similarity based product similarity/[FreeCoursesOnline.Me].url 133 Bytes42.27 - Measuring goodness of our solution AB testing/[FreeCoursesOnline.Me].url 133 Bytes42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/[FreeCoursesOnline.Me].url 133 Bytes42.3 - Amazon product advertising API/[FreeCoursesOnline.Me].url 133 Bytes42.4 - Data folders and paths/[FreeCoursesOnline.Me].url 133 Bytes42.5 - Overview of the data and Terminology/[FreeCoursesOnline.Me].url 133 Bytes42.6 - Data cleaning and understandingMissing data in various features/[FreeCoursesOnline.Me].url 133 Bytes42.7 - Understand duplicate rows/[FreeCoursesOnline.Me].url 133 Bytes42.8 - Remove duplicates Part 1/[FreeCoursesOnline.Me].url 133 Bytes42.9 - Remove duplicates Part 2/[FreeCoursesOnline.Me].url 133 Bytes43.1 - Businessreal world problem Problem definition/[FreeCoursesOnline.Me].url 133 Bytes43.10 - ML models – using byte files only Random Model/[FreeCoursesOnline.Me].url 133 Bytes43.11 - k-NN/[FreeCoursesOnline.Me].url 133 Bytes43.12 - Logistic regression/[FreeCoursesOnline.Me].url 133 Bytes43.13 - Random Forest and Xgboost/[FreeCoursesOnline.Me].url 133 Bytes43.14 - ASM Files Feature extraction & Multiprocessing/[FreeCoursesOnline.Me].url 133 Bytes43.15 - File-size feature/[FreeCoursesOnline.Me].url 133 Bytes43.16 - Univariate analysis/[FreeCoursesOnline.Me].url 133 Bytes43.17 - t-SNE analysis/[FreeCoursesOnline.Me].url 133 Bytes43.18 - ML models on ASM file features/[FreeCoursesOnline.Me].url 133 Bytes43.19 - Models on all features t-SNE/[FreeCoursesOnline.Me].url 133 Bytes43.2 - Businessreal world problem Objectives and constraints/[FreeCoursesOnline.Me].url 133 Bytes43.20 - Models on all features RandomForest and Xgboost/[FreeCoursesOnline.Me].url 133 Bytes43.21 - Assignments/[FreeCoursesOnline.Me].url 133 Bytes43.3 - Machine Learning problem mapping Data overview/[FreeCoursesOnline.Me].url 133 Bytes43.4 - Machine Learning problem mapping ML problem/[FreeCoursesOnline.Me].url 133 Bytes43.5 - Machine Learning problem mapping Train and test splitting/[FreeCoursesOnline.Me].url 133 Bytes43.6 - Exploratory Data Analysis Class distribution/[FreeCoursesOnline.Me].url 133 Bytes43.7 - Exploratory Data Analysis Feature extraction from byte files/[FreeCoursesOnline.Me].url 133 Bytes43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/[FreeCoursesOnline.Me].url 133 Bytes43.9 - Exploratory Data Analysis Train-Test class distribution/[FreeCoursesOnline.Me].url 133 Bytes44.1 - BusinessReal world problemProblem definition/[FreeCoursesOnline.Me].url 133 Bytes44.10 - Exploratory Data AnalysisCold start problem/[FreeCoursesOnline.Me].url 133 Bytes44.11 - Computing Similarity matricesUser-User similarity matrix/[FreeCoursesOnline.Me].url 133 Bytes44.12 - Computing Similarity matricesMovie-Movie similarity/[FreeCoursesOnline.Me].url 133 Bytes44.13 - Computing Similarity matricesDoes movie-movie similarity work/[FreeCoursesOnline.Me].url 133 Bytes44.14 - ML ModelsSurprise library/[FreeCoursesOnline.Me].url 133 Bytes44.15 - Overview of the modelling strategy/[FreeCoursesOnline.Me].url 133 Bytes44.16 - Data Sampling/[FreeCoursesOnline.Me].url 133 Bytes44.17 - Google drive with intermediate files/[FreeCoursesOnline.Me].url 133 Bytes44.18 - Featurizations for regression/[FreeCoursesOnline.Me].url 133 Bytes44.19 - Data transformation for Surprise/[FreeCoursesOnline.Me].url 133 Bytes44.2 - Objectives and constraints/[FreeCoursesOnline.Me].url 133 Bytes44.20 - Xgboost with 13 features/[FreeCoursesOnline.Me].url 133 Bytes44.21 - Surprise Baseline model/[FreeCoursesOnline.Me].url 133 Bytes44.22 - Xgboost + 13 features +Surprise baseline model/[FreeCoursesOnline.Me].url 133 Bytes44.23 - Surprise KNN predictors/[FreeCoursesOnline.Me].url 133 Bytes44.24 - Matrix Factorization models using Surprise/[FreeCoursesOnline.Me].url 133 Bytes44.25 - SVD ++ with implicit feedback/[FreeCoursesOnline.Me].url 133 Bytes44.26 - Final models with all features and predictors/[FreeCoursesOnline.Me].url 133 Bytes44.27 - Comparison between various models/[FreeCoursesOnline.Me].url 133 Bytes44.28 - Assignments/[FreeCoursesOnline.Me].url 133 Bytes44.3 - Mapping to an ML problemData overview/[FreeCoursesOnline.Me].url 133 Bytes44.4 - Mapping to an ML problemML problem formulation/[FreeCoursesOnline.Me].url 133 Bytes44.5 - Exploratory Data AnalysisData preprocessing/[FreeCoursesOnline.Me].url 133 Bytes44.6 - Exploratory Data AnalysisTemporal Train-Test split/[FreeCoursesOnline.Me].url 133 Bytes44.7 - Exploratory Data AnalysisPreliminary data analysis/[FreeCoursesOnline.Me].url 133 Bytes44.8 - Exploratory Data AnalysisSparse matrix representation/[FreeCoursesOnline.Me].url 133 Bytes44.9 - Exploratory Data AnalysisAverage ratings for various slices/[FreeCoursesOnline.Me].url 133 Bytes45.1 - BusinessReal world problem Overview/[FreeCoursesOnline.Me].url 133 Bytes45.10 - Univariate AnalysisVariation Feature/[FreeCoursesOnline.Me].url 133 Bytes45.11 - Univariate AnalysisText feature/[FreeCoursesOnline.Me].url 133 Bytes45.12 - Machine Learning ModelsData preparation/[FreeCoursesOnline.Me].url 133 Bytes45.13 - Baseline Model Naive Bayes/[FreeCoursesOnline.Me].url 133 Bytes45.14 - K-Nearest Neighbors Classification/[FreeCoursesOnline.Me].url 133 Bytes45.15 - Logistic Regression with class balancing/[FreeCoursesOnline.Me].url 133 Bytes45.16 - Logistic Regression without class balancing/[FreeCoursesOnline.Me].url 133 Bytes45.17 - Linear-SVM/[FreeCoursesOnline.Me].url 133 Bytes45.18 - Random-Forest with one-hot encoded features/[FreeCoursesOnline.Me].url 133 Bytes45.19 - Random-Forest with response-coded features/[FreeCoursesOnline.Me].url 133 Bytes45.2 - Business objectives and constraints/[FreeCoursesOnline.Me].url 133 Bytes45.20 - Stacking Classifier/[FreeCoursesOnline.Me].url 133 Bytes45.21 - Majority Voting classifier/[FreeCoursesOnline.Me].url 133 Bytes45.22 - Assignments/[FreeCoursesOnline.Me].url 133 Bytes45.3 - ML problem formulation Data/[FreeCoursesOnline.Me].url 133 Bytes45.4 - ML problem formulation Mapping real world to ML problem#/[FreeCoursesOnline.Me].url 133 Bytes45.4 - ML problem formulation Mapping real world to ML problem/[FreeCoursesOnline.Me].url 133 Bytes45.5 - ML problem formulation Train, CV and Test data construction/[FreeCoursesOnline.Me].url 133 Bytes45.6 - Exploratory Data AnalysisReading data & preprocessing/[FreeCoursesOnline.Me].url 133 Bytes45.7 - Exploratory Data AnalysisDistribution of Class-labels/[FreeCoursesOnline.Me].url 133 Bytes45.8 - Exploratory Data Analysis “Random” Model/[FreeCoursesOnline.Me].url 133 Bytes45.9 - Univariate AnalysisGene feature/[FreeCoursesOnline.Me].url 133 Bytes46.1 - BusinessReal world problem Overview/[FreeCoursesOnline.Me].url 133 Bytes46.10 - Data Cleaning Speed/[FreeCoursesOnline.Me].url 133 Bytes46.11 - Data Cleaning Distance/[FreeCoursesOnline.Me].url 133 Bytes46.12 - Data Cleaning Fare/[FreeCoursesOnline.Me].url 133 Bytes46.13 - Data Cleaning Remove all outlierserroneous points/[FreeCoursesOnline.Me].url 133 Bytes46.14 - Data PreparationClusteringSegmentation/[FreeCoursesOnline.Me].url 133 Bytes46.15 - Data PreparationTime binning/[FreeCoursesOnline.Me].url 133 Bytes46.16 - Data PreparationSmoothing time-series data/[FreeCoursesOnline.Me].url 133 Bytes46.17 - Data PreparationSmoothing time-series data cont/[FreeCoursesOnline.Me].url 133 Bytes46.18 - Data Preparation Time series and Fourier transforms/[FreeCoursesOnline.Me].url 133 Bytes46.19 - Ratios and previous-time-bin values/[FreeCoursesOnline.Me].url 133 Bytes46.2 - Objectives and Constraints/[FreeCoursesOnline.Me].url 133 Bytes46.20 - Simple moving average/[FreeCoursesOnline.Me].url 133 Bytes46.21 - Weighted Moving average/[FreeCoursesOnline.Me].url 133 Bytes46.22 - Exponential weighted moving average/[FreeCoursesOnline.Me].url 133 Bytes46.23 - Results/[FreeCoursesOnline.Me].url 133 Bytes46.24 - Regression models Train-Test split & Features/[FreeCoursesOnline.Me].url 133 Bytes46.25 - Linear regression/[FreeCoursesOnline.Me].url 133 Bytes46.26 - Random Forest regression/[FreeCoursesOnline.Me].url 133 Bytes46.27 - Xgboost Regression/[FreeCoursesOnline.Me].url 133 Bytes46.28 - Model comparison/[FreeCoursesOnline.Me].url 133 Bytes46.29 - Assignment/[FreeCoursesOnline.Me].url 133 Bytes46.3 - Mapping to ML problem Data/[FreeCoursesOnline.Me].url 133 Bytes46.4 - Mapping to ML problem dask dataframes/[FreeCoursesOnline.Me].url 133 Bytes46.5 - Mapping to ML problem FieldsFeatures/[FreeCoursesOnline.Me].url 133 Bytes46.6 - Mapping to ML problem Time series forecastingRegression/[FreeCoursesOnline.Me].url 133 Bytes46.7 - Mapping to ML problem Performance metrics/[FreeCoursesOnline.Me].url 133 Bytes46.8 - Data Cleaning Latitude and Longitude data/[FreeCoursesOnline.Me].url 133 Bytes46.9 - Data Cleaning Trip Duration/[FreeCoursesOnline.Me].url 133 Bytes47.1 - History of Neural networks and Deep Learning/[FreeCoursesOnline.Me].url 133 Bytes47.10 - Backpropagation/[FreeCoursesOnline.Me].url 133 Bytes47.11 - Activation functions/[FreeCoursesOnline.Me].url 133 Bytes47.12 - Vanishing Gradient problem/[FreeCoursesOnline.Me].url 133 Bytes47.13 - Bias-Variance tradeoff/[FreeCoursesOnline.Me].url 133 Bytes47.14 - Decision surfaces Playground/[FreeCoursesOnline.Me].url 133 Bytes47.2 - How Biological Neurons work/[FreeCoursesOnline.Me].url 133 Bytes47.3 - Growth of biological neural networks/[FreeCoursesOnline.Me].url 133 Bytes47.4 - Diagrammatic representation Logistic Regression and Perceptron/[FreeCoursesOnline.Me].url 133 Bytes47.5 - Multi-Layered Perceptron (MLP)/[FreeCoursesOnline.Me].url 133 Bytes47.6 - Notation/[FreeCoursesOnline.Me].url 133 Bytes47.7 - Training a single-neuron model/[FreeCoursesOnline.Me].url 133 Bytes47.8 - Training an MLP Chain Rule/[FreeCoursesOnline.Me].url 133 Bytes47.9 - Training an MLPMemoization/[FreeCoursesOnline.Me].url 133 Bytes48.1 - Deep Multi-layer perceptrons1980s to 2010s/[FreeCoursesOnline.Me].url 133 Bytes48.10 - Nesterov Accelerated Gradient (NAG)/[FreeCoursesOnline.Me].url 133 Bytes48.11 - OptimizersAdaGrad/[FreeCoursesOnline.Me].url 133 Bytes48.12 - Optimizers Adadelta andRMSProp/[FreeCoursesOnline.Me].url 133 Bytes48.13 - Adam/[FreeCoursesOnline.Me].url 133 Bytes48.14 - Which algorithm to choose when/[FreeCoursesOnline.Me].url 133 Bytes48.15 - Gradient Checking and clipping/[FreeCoursesOnline.Me].url 133 Bytes48.16 - Softmax and Cross-entropy for multi-class classification/[FreeCoursesOnline.Me].url 133 Bytes48.17 - How to train a Deep MLP/[FreeCoursesOnline.Me].url 133 Bytes48.18 - Auto Encoders/[FreeCoursesOnline.Me].url 133 Bytes48.19 - Word2Vec CBOW/[FreeCoursesOnline.Me].url 133 Bytes48.2 - Dropout layers & Regularization/[FreeCoursesOnline.Me].url 133 Bytes48.20 - Word2Vec Skip-gram/[FreeCoursesOnline.Me].url 133 Bytes48.21 - Word2Vec Algorithmic Optimizations/[FreeCoursesOnline.Me].url 133 Bytes48.3 - Rectified Linear Units (ReLU)/[FreeCoursesOnline.Me].url 133 Bytes48.4 - Weight initialization/[FreeCoursesOnline.Me].url 133 Bytes48.5 - Batch Normalization/[FreeCoursesOnline.Me].url 133 Bytes48.6 - OptimizersHill-descent analogy in 2D/[FreeCoursesOnline.Me].url 133 Bytes48.7 - OptimizersHill descent in 3D and contours/[FreeCoursesOnline.Me].url 133 Bytes48.8 - SGD Recap/[FreeCoursesOnline.Me].url 133 Bytes48.9 - Batch SGD with momentum/[FreeCoursesOnline.Me].url 133 Bytes49.1 - Tensorflow and Keras overview/[FreeCoursesOnline.Me].url 133 Bytes49.10 - Model 3 Batch Normalization/[FreeCoursesOnline.Me].url 133 Bytes49.11 - Model 4 Dropout/[FreeCoursesOnline.Me].url 133 Bytes49.12 - MNIST classification in Keras/[FreeCoursesOnline.Me].url 133 Bytes49.13 - Hyperparameter tuning in Keras/[FreeCoursesOnline.Me].url 133 Bytes49.14 - Exercise Try different MLP architectures on MNIST dataset/[FreeCoursesOnline.Me].url 133 Bytes49.2 - GPU vs CPU for Deep Learning/[FreeCoursesOnline.Me].url 133 Bytes49.3 - Google Colaboratory/[FreeCoursesOnline.Me].url 133 Bytes49.4 - Install TensorFlow/[FreeCoursesOnline.Me].url 133 Bytes49.5 - Online documentation and tutorials/[FreeCoursesOnline.Me].url 133 Bytes49.6 - Softmax Classifier on MNIST dataset/[FreeCoursesOnline.Me].url 133 Bytes49.7 - MLP Initialization/[FreeCoursesOnline.Me].url 133 Bytes49.8 - Model 1 Sigmoid activation/[FreeCoursesOnline.Me].url 133 Bytes49.9 - Model 2 ReLU activation/[FreeCoursesOnline.Me].url 133 Bytes5.1 - Numpy Introduction/[FreeCoursesOnline.Me].url 133 Bytes5.2 - Numerical operations on Numpy/[FreeCoursesOnline.Me].url 133 Bytes50.1 - Biological inspiration Visual Cortex/[FreeCoursesOnline.Me].url 133 Bytes50.10 - Data Augmentation/[FreeCoursesOnline.Me].url 133 Bytes50.11 - Convolution Layers in Keras/[FreeCoursesOnline.Me].url 133 Bytes50.12 - AlexNet/[FreeCoursesOnline.Me].url 133 Bytes50.13 - VGGNet/[FreeCoursesOnline.Me].url 133 Bytes50.14 - Residual Network/[FreeCoursesOnline.Me].url 133 Bytes50.15 - Inception Network/[FreeCoursesOnline.Me].url 133 Bytes50.16 - What is Transfer learning/[FreeCoursesOnline.Me].url 133 Bytes50.17 - Code example Cats vs Dogs/[FreeCoursesOnline.Me].url 133 Bytes50.18 - Code Example MNIST dataset/[FreeCoursesOnline.Me].url 133 Bytes50.19 - Assignment Try various CNN networks on MNIST dataset#/[FreeCoursesOnline.Me].url 133 Bytes50.2 - ConvolutionEdge Detection on images/[FreeCoursesOnline.Me].url 133 Bytes50.3 - ConvolutionPadding and strides/[FreeCoursesOnline.Me].url 133 Bytes50.4 - Convolution over RGB images/[FreeCoursesOnline.Me].url 133 Bytes50.5 - Convolutional layer/[FreeCoursesOnline.Me].url 133 Bytes50.6 - Max-pooling/[FreeCoursesOnline.Me].url 133 Bytes50.7 - CNN Training Optimization/[FreeCoursesOnline.Me].url 133 Bytes50.8 - Example CNN LeNet [1998]/[FreeCoursesOnline.Me].url 133 Bytes50.9 - ImageNet dataset/[FreeCoursesOnline.Me].url 133 Bytes51.1 - Why RNNs/[FreeCoursesOnline.Me].url 133 Bytes51.10 - Code example IMDB Sentiment classification/[FreeCoursesOnline.Me].url 133 Bytes51.11 - Exercise Amazon Fine Food reviews LSTM model/[FreeCoursesOnline.Me].url 133 Bytes51.2 - Recurrent Neural Network/[FreeCoursesOnline.Me].url 133 Bytes51.3 - Training RNNs Backprop/[FreeCoursesOnline.Me].url 133 Bytes51.4 - Types of RNNs/[FreeCoursesOnline.Me].url 133 Bytes51.5 - Need for LSTMGRU/[FreeCoursesOnline.Me].url 133 Bytes51.6 - LSTM/[FreeCoursesOnline.Me].url 133 Bytes51.7 - GRUs/[FreeCoursesOnline.Me].url 133 Bytes51.8 - Deep RNN/[FreeCoursesOnline.Me].url 133 Bytes51.9 - Bidirectional RNN/[FreeCoursesOnline.Me].url 133 Bytes52.1 - Questions and Answers/[FreeCoursesOnline.Me].url 133 Bytes53.1 - Self Driving Car Problem definition/[FreeCoursesOnline.Me].url 133 Bytes53.10 - NVIDIA’s end to end CNN model/[FreeCoursesOnline.Me].url 133 Bytes53.11 - Train the model/[FreeCoursesOnline.Me].url 133 Bytes53.12 - Test and visualize the output/[FreeCoursesOnline.Me].url 133 Bytes53.13 - Extensions/[FreeCoursesOnline.Me].url 133 Bytes53.14 - Assignment/[FreeCoursesOnline.Me].url 133 Bytes53.2 - Datasets#/[FreeCoursesOnline.Me].url 133 Bytes53.2 - Datasets/[FreeCoursesOnline.Me].url 133 Bytes53.3 - Data understanding & Analysis Files and folders/[FreeCoursesOnline.Me].url 133 Bytes53.4 - Dash-cam images and steering angles/[FreeCoursesOnline.Me].url 133 Bytes53.5 - Split the dataset Train vs Test/[FreeCoursesOnline.Me].url 133 Bytes53.6 - EDA Steering angles/[FreeCoursesOnline.Me].url 133 Bytes53.7 - Mean Baseline model simple/[FreeCoursesOnline.Me].url 133 Bytes53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/[FreeCoursesOnline.Me].url 133 Bytes53.9 - Batch load the dataset/[FreeCoursesOnline.Me].url 133 Bytes54.1 - Real-world problem/[FreeCoursesOnline.Me].url 133 Bytes54.10 - MIDI music generation/[FreeCoursesOnline.Me].url 133 Bytes54.11 - Survey blog/[FreeCoursesOnline.Me].url 133 Bytes54.2 - Music representation/[FreeCoursesOnline.Me].url 133 Bytes54.3 - Char-RNN with abc-notation Char-RNN model/[FreeCoursesOnline.Me].url 133 Bytes54.4 - Char-RNN with abc-notation Data preparation/[FreeCoursesOnline.Me].url 133 Bytes54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/[FreeCoursesOnline.Me].url 133 Bytes54.6 - Char-RNN with abc-notation State full RNN/[FreeCoursesOnline.Me].url 133 Bytes54.7 - Char-RNN with abc-notation Model architecture,Model training/[FreeCoursesOnline.Me].url 133 Bytes54.8 - Char-RNN with abc-notation Music generation/[FreeCoursesOnline.Me].url 133 Bytes54.9 - Char-RNN with abc-notation Generate tabla music/[FreeCoursesOnline.Me].url 133 Bytes55.1 - Human Activity Recognition Problem definition/[FreeCoursesOnline.Me].url 133 Bytes55.2 - Dataset understanding/[FreeCoursesOnline.Me].url 133 Bytes55.3 - Data cleaning & preprocessing/[FreeCoursesOnline.Me].url 133 Bytes55.4 - EDAUnivariate analysis/[FreeCoursesOnline.Me].url 133 Bytes55.5 - EDAData visualization using t-SNE/[FreeCoursesOnline.Me].url 133 Bytes55.6 - Classical ML models/[FreeCoursesOnline.Me].url 133 Bytes55.7 - Deep-learning Model/[FreeCoursesOnline.Me].url 133 Bytes55.8 - Exercise Build deeper LSTM models and hyper-param tune them/[FreeCoursesOnline.Me].url 133 Bytes56.1 - Problem definition/[FreeCoursesOnline.Me].url 133 Bytes56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/[FreeCoursesOnline.Me].url 133 Bytes56.11 - PageRank/[FreeCoursesOnline.Me].url 133 Bytes56.12 - Shortest Path/[FreeCoursesOnline.Me].url 133 Bytes56.13 - Connected-components/[FreeCoursesOnline.Me].url 133 Bytes56.14 - Adar Index/[FreeCoursesOnline.Me].url 133 Bytes56.15 - Kartz Centrality/[FreeCoursesOnline.Me].url 133 Bytes56.16 - HITS Score/[FreeCoursesOnline.Me].url 133 Bytes56.17 - SVD/[FreeCoursesOnline.Me].url 133 Bytes56.18 - Weight features/[FreeCoursesOnline.Me].url 133 Bytes56.19 - Modeling/[FreeCoursesOnline.Me].url 133 Bytes56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/[FreeCoursesOnline.Me].url 133 Bytes56.3 - Data format & Limitations/[FreeCoursesOnline.Me].url 133 Bytes56.4 - Mapping to a supervised classification problem/[FreeCoursesOnline.Me].url 133 Bytes56.5 - Business constraints & Metrics/[FreeCoursesOnline.Me].url 133 Bytes56.6 - EDABasic Stats/[FreeCoursesOnline.Me].url 133 Bytes56.7 - EDAFollower and following stats/[FreeCoursesOnline.Me].url 133 Bytes56.8 - EDABinary Classification Task/[FreeCoursesOnline.Me].url 133 Bytes56.9 - EDATrain and test split/[FreeCoursesOnline.Me].url 133 Bytes57.1 - Introduction to Databases/[FreeCoursesOnline.Me].url 133 Bytes57.10 - ORDER BY/[FreeCoursesOnline.Me].url 133 Bytes57.11 - DISTINCT/[FreeCoursesOnline.Me].url 133 Bytes57.12 - WHERE, Comparison operators, NULL/[FreeCoursesOnline.Me].url 133 Bytes57.13 - Logical Operators/[FreeCoursesOnline.Me].url 133 Bytes57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/[FreeCoursesOnline.Me].url 133 Bytes57.15 - GROUP BY/[FreeCoursesOnline.Me].url 133 Bytes57.16 - HAVING/[FreeCoursesOnline.Me].url 133 Bytes57.17 - Order of keywords#/[FreeCoursesOnline.Me].url 133 Bytes57.18 - Join and Natural Join/[FreeCoursesOnline.Me].url 133 Bytes57.19 - Inner, Left, Right and Outer joins/[FreeCoursesOnline.Me].url 133 Bytes57.2 - Why SQL/[FreeCoursesOnline.Me].url 133 Bytes57.20 - Sub QueriesNested QueriesInner Queries/[FreeCoursesOnline.Me].url 133 Bytes57.21 - DMLINSERT/[FreeCoursesOnline.Me].url 133 Bytes57.22 - DMLUPDATE , DELETE/[FreeCoursesOnline.Me].url 133 Bytes57.23 - DDLCREATE TABLE/[FreeCoursesOnline.Me].url 133 Bytes57.24 - DDLALTER ADD, MODIFY, DROP/[FreeCoursesOnline.Me].url 133 Bytes57.25 - DDLDROP TABLE, TRUNCATE, DELETE/[FreeCoursesOnline.Me].url 133 Bytes57.26 - Data Control Language GRANT, REVOKE/[FreeCoursesOnline.Me].url 133 Bytes57.27 - Learning resources/[FreeCoursesOnline.Me].url 133 Bytes57.3 - Execution of an SQL statement/[FreeCoursesOnline.Me].url 133 Bytes57.4 - IMDB dataset/[FreeCoursesOnline.Me].url 133 Bytes57.5 - Installing MySQL/[FreeCoursesOnline.Me].url 133 Bytes57.6 - Load IMDB data/[FreeCoursesOnline.Me].url 133 Bytes57.7 - USE, DESCRIBE, SHOW TABLES/[FreeCoursesOnline.Me].url 133 Bytes57.8 - SELECT/[FreeCoursesOnline.Me].url 133 Bytes57.9 - LIMIT, OFFSET/[FreeCoursesOnline.Me].url 133 Bytes58.1 - AD-Click Predicition/[FreeCoursesOnline.Me].url 133 Bytes59.1 - Revision Questions/[FreeCoursesOnline.Me].url 133 Bytes59.2 - Questions/[FreeCoursesOnline.Me].url 133 Bytes59.3 - External resources for Interview Questions/[FreeCoursesOnline.Me].url 133 Bytes6.1 - Getting started with Matplotlib/[FreeCoursesOnline.Me].url 133 Bytes7.1 - Getting started with pandas/[FreeCoursesOnline.Me].url 133 Bytes7.2 - Data Frame Basics/[FreeCoursesOnline.Me].url 133 Bytes7.3 - Key Operations on Data Frames/[FreeCoursesOnline.Me].url 133 Bytes8.1 - Space and Time Complexity Find largest number in a list/[FreeCoursesOnline.Me].url 133 Bytes8.2 - Binary search/[FreeCoursesOnline.Me].url 133 Bytes8.3 - Find elements common in two lists/[FreeCoursesOnline.Me].url 133 Bytes8.4 - Find elements common in two lists using a HashtableDict/[FreeCoursesOnline.Me].url 133 Bytes9.1 - Introduction to IRIS dataset and 2D scatter plot/[FreeCoursesOnline.Me].url 133 Bytes9.10 - Percentiles and Quantiles/[FreeCoursesOnline.Me].url 133 Bytes9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/[FreeCoursesOnline.Me].url 133 Bytes9.12 - Box-plot with Whiskers/[FreeCoursesOnline.Me].url 133 Bytes9.13 - Violin Plots/[FreeCoursesOnline.Me].url 133 Bytes9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/[FreeCoursesOnline.Me].url 133 Bytes9.15 - Multivariate Probability Density, Contour Plot/[FreeCoursesOnline.Me].url 133 Bytes9.16 - Exercise Perform EDA on Haberman dataset/[FreeCoursesOnline.Me].url 133 Bytes9.2 - 3D scatter plot/[FreeCoursesOnline.Me].url 133 Bytes9.3 - Pair plots/[FreeCoursesOnline.Me].url 133 Bytes9.4 - Limitations of Pair Plots/[FreeCoursesOnline.Me].url 133 Bytes9.5 - Histogram and Introduction to PDF(Probability Density Function)/[FreeCoursesOnline.Me].url 133 Bytes9.6 - Univariate Analysis using PDF/[FreeCoursesOnline.Me].url 133 Bytes9.7 - CDF(Cumulative Distribution Function)/[FreeCoursesOnline.Me].url 133 Bytes9.8 - Mean, Variance and Standard Deviation/[FreeCoursesOnline.Me].url 133 Bytes9.9 - Median/[FreeCoursesOnline.Me].url 133 Bytes[FCS Forum].url 133 Bytes1.1 - How to Learn from Appliedaicourse/[FreeTutorials.Eu].url 129 Bytes1.2 - How the Job Guarantee program works/[FreeTutorials.Eu].url 129 Bytes10.1 - Why learn it/[FreeTutorials.Eu].url 129 Bytes10.10 - Hyper Cube,Hyper Cuboid/[FreeTutorials.Eu].url 129 Bytes10.11 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/[FreeTutorials.Eu].url 129 Bytes10.3 - Dot Product and Angle between 2 Vectors/[FreeTutorials.Eu].url 129 Bytes10.4 - Projection and Unit Vector/[FreeTutorials.Eu].url 129 Bytes10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/[FreeTutorials.Eu].url 129 Bytes10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/[FreeTutorials.Eu].url 129 Bytes10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/[FreeTutorials.Eu].url 129 Bytes10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/[FreeTutorials.Eu].url 129 Bytes10.9 - Square ,Rectangle/[FreeTutorials.Eu].url 129 Bytes11.1 - Introduction to Probability and Statistics/[FreeTutorials.Eu].url 129 Bytes11.10 - How distributions are used/[FreeTutorials.Eu].url 129 Bytes11.11 - Chebyshev’s inequality/[FreeTutorials.Eu].url 129 Bytes11.12 - Discrete and Continuous Uniform distributions/[FreeTutorials.Eu].url 129 Bytes11.13 - How to randomly sample data points (Uniform Distribution)/[FreeTutorials.Eu].url 129 Bytes11.14 - Bernoulli and Binomial Distribution/[FreeTutorials.Eu].url 129 Bytes11.15 - Log Normal Distribution/[FreeTutorials.Eu].url 129 Bytes11.16 - Power law distribution/[FreeTutorials.Eu].url 129 Bytes11.17 - Box cox transform/[FreeTutorials.Eu].url 129 Bytes11.18 - Applications of non-gaussian distributions/[FreeTutorials.Eu].url 129 Bytes11.19 - Co-variance/[FreeTutorials.Eu].url 129 Bytes11.2 - Population and Sample/[FreeTutorials.Eu].url 129 Bytes11.20 - Pearson Correlation Coefficient/[FreeTutorials.Eu].url 129 Bytes11.21 - Spearman Rank Correlation Coefficient/[FreeTutorials.Eu].url 129 Bytes11.22 - Correlation vs Causation/[FreeTutorials.Eu].url 129 Bytes11.23 - How to use correlations/[FreeTutorials.Eu].url 129 Bytes11.24 - Confidence interval (C.I) Introduction/[FreeTutorials.Eu].url 129 Bytes11.25 - Computing confidence interval given the underlying distribution/[FreeTutorials.Eu].url 129 Bytes11.26 - C.I for mean of a normal random variable/[FreeTutorials.Eu].url 129 Bytes11.27 - Confidence interval using bootstrapping/[FreeTutorials.Eu].url 129 Bytes11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/[FreeTutorials.Eu].url 129 Bytes11.29 - Hypothesis Testing Intution with coin toss example/[FreeTutorials.Eu].url 129 Bytes11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/[FreeTutorials.Eu].url 129 Bytes11.30 - Resampling and permutation test/[FreeTutorials.Eu].url 129 Bytes11.31 - K-S Test for similarity of two distributions/[FreeTutorials.Eu].url 129 Bytes11.32 - Code Snippet K-S Test/[FreeTutorials.Eu].url 129 Bytes11.33 - Hypothesis testing another example/[FreeTutorials.Eu].url 129 Bytes11.34 - Resampling and Permutation test another example/[FreeTutorials.Eu].url 129 Bytes11.35 - How to use hypothesis testing/[FreeTutorials.Eu].url 129 Bytes11.36 - Proportional Sampling/[FreeTutorials.Eu].url 129 Bytes11.37 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/[FreeTutorials.Eu].url 129 Bytes11.5 - Symmetric distribution, Skewness and Kurtosis/[FreeTutorials.Eu].url 129 Bytes11.6 - Standard normal variate (Z) and standardization/[FreeTutorials.Eu].url 129 Bytes11.7 - Kernel density estimation/[FreeTutorials.Eu].url 129 Bytes11.8 - Sampling distribution & Central Limit theorem/[FreeTutorials.Eu].url 129 Bytes11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/[FreeTutorials.Eu].url 129 Bytes12.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes13.1 - What is Dimensionality reduction/[FreeTutorials.Eu].url 129 Bytes13.10 - Code to Load MNIST Data Set/[FreeTutorials.Eu].url 129 Bytes13.2 - Row Vector and Column Vector/[FreeTutorials.Eu].url 129 Bytes13.3 - How to represent a data set/[FreeTutorials.Eu].url 129 Bytes13.4 - How to represent a dataset as a Matrix/[FreeTutorials.Eu].url 129 Bytes13.5 - Data Preprocessing Feature Normalisation/[FreeTutorials.Eu].url 129 Bytes13.6 - Mean of a data matrix/[FreeTutorials.Eu].url 129 Bytes13.7 - Data Preprocessing Column Standardization/[FreeTutorials.Eu].url 129 Bytes13.8 - Co-variance of a Data Matrix/[FreeTutorials.Eu].url 129 Bytes13.9 - MNIST dataset (784 dimensional)/[FreeTutorials.Eu].url 129 Bytes14.1 - Why learn PCA/[FreeTutorials.Eu].url 129 Bytes14.10 - PCA for dimensionality reduction (not-visualization)/[FreeTutorials.Eu].url 129 Bytes14.2 - Geometric intuition of PCA/[FreeTutorials.Eu].url 129 Bytes14.3 - Mathematical objective function of PCA/[FreeTutorials.Eu].url 129 Bytes14.4 - Alternative formulation of PCA Distance minimization/[FreeTutorials.Eu].url 129 Bytes14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/[FreeTutorials.Eu].url 129 Bytes14.6 - PCA for Dimensionality Reduction and Visualization/[FreeTutorials.Eu].url 129 Bytes14.7 - Visualize MNIST dataset/[FreeTutorials.Eu].url 129 Bytes14.8 - Limitations of PCA/[FreeTutorials.Eu].url 129 Bytes14.9 - PCA Code example/[FreeTutorials.Eu].url 129 Bytes15.1 - What is t-SNE/[FreeTutorials.Eu].url 129 Bytes15.2 - Neighborhood of a point, Embedding/[FreeTutorials.Eu].url 129 Bytes15.3 - Geometric intuition of t-SNE/[FreeTutorials.Eu].url 129 Bytes15.4 - Crowding Problem/[FreeTutorials.Eu].url 129 Bytes15.5 - How to apply t-SNE and interpret its output/[FreeTutorials.Eu].url 129 Bytes15.6 - t-SNE on MNIST/[FreeTutorials.Eu].url 129 Bytes15.7 - Code example of t-SNE/[FreeTutorials.Eu].url 129 Bytes15.8 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes16.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes17.1 - Dataset overview Amazon Fine Food reviews(EDA)/[FreeTutorials.Eu].url 129 Bytes17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/[FreeTutorials.Eu].url 129 Bytes17.11 - Bag of Words( Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.12 - Text Preprocessing( Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.13 - Bi-Grams and n-grams (Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.14 - TF-IDF (Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.15 - Word2Vec (Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/[FreeTutorials.Eu].url 129 Bytes17.17 - Assignment-2 Apply t-SNE/[FreeTutorials.Eu].url 129 Bytes17.2 - Data Cleaning Deduplication/[FreeTutorials.Eu].url 129 Bytes17.3 - Why convert text to a vector/[FreeTutorials.Eu].url 129 Bytes17.4 - Bag of Words (BoW)/[FreeTutorials.Eu].url 129 Bytes17.5 - Text Preprocessing Stemming/[FreeTutorials.Eu].url 129 Bytes17.6 - uni-gram, bi-gram, n-grams/[FreeTutorials.Eu].url 129 Bytes17.7 - tf-idf (term frequency- inverse document frequency)/[FreeTutorials.Eu].url 129 Bytes17.8 - Why use log in IDF/[FreeTutorials.Eu].url 129 Bytes17.9 - Word2Vec/[FreeTutorials.Eu].url 129 Bytes18.1 - How “Classification” works/[FreeTutorials.Eu].url 129 Bytes18.10 - KNN Limitations/[FreeTutorials.Eu].url 129 Bytes18.11 - Decision surface for K-NN as K changes/[FreeTutorials.Eu].url 129 Bytes18.12 - Overfitting and Underfitting/[FreeTutorials.Eu].url 129 Bytes18.13 - Need for Cross validation/[FreeTutorials.Eu].url 129 Bytes18.14 - K-fold cross validation/[FreeTutorials.Eu].url 129 Bytes18.15 - Visualizing train, validation and test datasets/[FreeTutorials.Eu].url 129 Bytes18.16 - How to determine overfitting and underfitting/[FreeTutorials.Eu].url 129 Bytes18.17 - Time based splitting/[FreeTutorials.Eu].url 129 Bytes18.18 - k-NN for regression/[FreeTutorials.Eu].url 129 Bytes18.19 - Weighted k-NN/[FreeTutorials.Eu].url 129 Bytes18.2 - Data matrix notation/[FreeTutorials.Eu].url 129 Bytes18.20 - Voronoi diagram/[FreeTutorials.Eu].url 129 Bytes18.21 - Binary search tree/[FreeTutorials.Eu].url 129 Bytes18.22 - How to build a kd-tree/[FreeTutorials.Eu].url 129 Bytes18.23 - Find nearest neighbours using kd-tree/[FreeTutorials.Eu].url 129 Bytes18.24 - Limitations of Kd tree/[FreeTutorials.Eu].url 129 Bytes18.25 - Extensions/[FreeTutorials.Eu].url 129 Bytes18.26 - Hashing vs LSH/[FreeTutorials.Eu].url 129 Bytes18.27 - LSH for cosine similarity/[FreeTutorials.Eu].url 129 Bytes18.28 - LSH for euclidean distance/[FreeTutorials.Eu].url 129 Bytes18.29 - Probabilistic class label/[FreeTutorials.Eu].url 129 Bytes18.3 - Classification vs Regression (examples)/[FreeTutorials.Eu].url 129 Bytes18.30 - Code SampleDecision boundary/[FreeTutorials.Eu].url 129 Bytes18.31 - Code SampleCross Validation/[FreeTutorials.Eu].url 129 Bytes18.32 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes18.4 - K-Nearest Neighbours Geometric intuition with a toy example/[FreeTutorials.Eu].url 129 Bytes18.5 - Failure cases of KNN/[FreeTutorials.Eu].url 129 Bytes18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/[FreeTutorials.Eu].url 129 Bytes18.7 - Cosine Distance & Cosine Similarity/[FreeTutorials.Eu].url 129 Bytes18.8 - How to measure the effectiveness of k-NN/[FreeTutorials.Eu].url 129 Bytes18.9 - TestEvaluation time and space complexity/[FreeTutorials.Eu].url 129 Bytes19.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes2.1 - Python, Anaconda and relevant packages installations/[FreeTutorials.Eu].url 129 Bytes2.10 - Control flow for loop/[FreeTutorials.Eu].url 129 Bytes2.11 - Control flow break and continue/[FreeTutorials.Eu].url 129 Bytes2.2 - Why learn Python/[FreeTutorials.Eu].url 129 Bytes2.3 - Keywords and identifiers/[FreeTutorials.Eu].url 129 Bytes2.4 - comments, indentation and statements/[FreeTutorials.Eu].url 129 Bytes2.5 - Variables and data types in Python/[FreeTutorials.Eu].url 129 Bytes2.6 - Standard Input and Output/[FreeTutorials.Eu].url 129 Bytes2.7 - Operators/[FreeTutorials.Eu].url 129 Bytes2.8 - Control flow if else/[FreeTutorials.Eu].url 129 Bytes2.9 - Control flow while loop/[FreeTutorials.Eu].url 129 Bytes20.1 - Introduction/[FreeTutorials.Eu].url 129 Bytes20.10 - Local reachability-density(A)/[FreeTutorials.Eu].url 129 Bytes20.11 - Local outlier Factor(A)/[FreeTutorials.Eu].url 129 Bytes20.12 - Impact of Scale & Column standardization/[FreeTutorials.Eu].url 129 Bytes20.13 - Interpretability/[FreeTutorials.Eu].url 129 Bytes20.14 - Feature Importance and Forward Feature selection/[FreeTutorials.Eu].url 129 Bytes20.15 - Handling categorical and numerical features/[FreeTutorials.Eu].url 129 Bytes20.16 - Handling missing values by imputation/[FreeTutorials.Eu].url 129 Bytes20.17 - curse of dimensionality/[FreeTutorials.Eu].url 129 Bytes20.18 - Bias-Variance tradeoff/[FreeTutorials.Eu].url 129 Bytes20.19 - Intuitive understanding of bias-variance/[FreeTutorials.Eu].url 129 Bytes20.2 - Imbalanced vs balanced dataset/[FreeTutorials.Eu].url 129 Bytes20.20 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes20.21 - best and wrost case of algorithm/[FreeTutorials.Eu].url 129 Bytes20.3 - Multi-class classification/[FreeTutorials.Eu].url 129 Bytes20.4 - k-NN, given a distance or similarity matrix/[FreeTutorials.Eu].url 129 Bytes20.5 - Train and test set differences/[FreeTutorials.Eu].url 129 Bytes20.6 - Impact of outliers/[FreeTutorials.Eu].url 129 Bytes20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/[FreeTutorials.Eu].url 129 Bytes20.8 - k distance/[FreeTutorials.Eu].url 129 Bytes20.9 - Reachability-Distance(A,B)/[FreeTutorials.Eu].url 129 Bytes21.1 - Accuracy/[FreeTutorials.Eu].url 129 Bytes21.10 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes21.2 - Confusion matrix, TPR, FPR, FNR, TNR/[FreeTutorials.Eu].url 129 Bytes21.3 - Precision and recall, F1-score/[FreeTutorials.Eu].url 129 Bytes21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/[FreeTutorials.Eu].url 129 Bytes21.5 - Log-loss/[FreeTutorials.Eu].url 129 Bytes21.6 - R-SquaredCoefficient of determination/[FreeTutorials.Eu].url 129 Bytes21.7 - Median absolute deviation (MAD)/[FreeTutorials.Eu].url 129 Bytes21.8 - Distribution of errors/[FreeTutorials.Eu].url 129 Bytes21.9 - Assignment-3 Apply k-Nearest Neighbor/[FreeTutorials.Eu].url 129 Bytes22.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes23.1 - Conditional probability/[FreeTutorials.Eu].url 129 Bytes23.10 - Bias and Variance tradeoff/[FreeTutorials.Eu].url 129 Bytes23.11 - Feature importance and interpretability/[FreeTutorials.Eu].url 129 Bytes23.12 - Imbalanced data/[FreeTutorials.Eu].url 129 Bytes23.13 - Outliers/[FreeTutorials.Eu].url 129 Bytes23.14 - Missing values/[FreeTutorials.Eu].url 129 Bytes23.15 - Handling Numerical features (Gaussian NB)/[FreeTutorials.Eu].url 129 Bytes23.16 - Multiclass classification/[FreeTutorials.Eu].url 129 Bytes23.17 - Similarity or Distance matrix/[FreeTutorials.Eu].url 129 Bytes23.18 - Large dimensionality/[FreeTutorials.Eu].url 129 Bytes23.19 - Best and worst cases/[FreeTutorials.Eu].url 129 Bytes23.2 - Independent vs Mutually exclusive events/[FreeTutorials.Eu].url 129 Bytes23.20 - Code example/[FreeTutorials.Eu].url 129 Bytes23.21 - Assignment-4 Apply Naive Bayes/[FreeTutorials.Eu].url 129 Bytes23.22 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes23.3 - Bayes Theorem with examples/[FreeTutorials.Eu].url 129 Bytes23.4 - Exercise problems on Bayes Theorem/[FreeTutorials.Eu].url 129 Bytes23.5 - Naive Bayes algorithm/[FreeTutorials.Eu].url 129 Bytes23.6 - Toy example Train and test stages/[FreeTutorials.Eu].url 129 Bytes23.7 - Naive Bayes on Text data/[FreeTutorials.Eu].url 129 Bytes23.8 - LaplaceAdditive Smoothing/[FreeTutorials.Eu].url 129 Bytes23.9 - Log-probabilities for numerical stability/[FreeTutorials.Eu].url 129 Bytes24.1 - Geometric intuition of Logistic Regression/[FreeTutorials.Eu].url 129 Bytes24.10 - Column Standardization/[FreeTutorials.Eu].url 129 Bytes24.11 - Feature importance and Model interpretability/[FreeTutorials.Eu].url 129 Bytes24.12 - Collinearity of features/[FreeTutorials.Eu].url 129 Bytes24.13 - TestRun time space and time complexity/[FreeTutorials.Eu].url 129 Bytes24.14 - Real world cases/[FreeTutorials.Eu].url 129 Bytes24.15 - Non-linearly separable data & feature engineering/[FreeTutorials.Eu].url 129 Bytes24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/[FreeTutorials.Eu].url 129 Bytes24.17 - Assignment-5 Apply Logistic Regression/[FreeTutorials.Eu].url 129 Bytes24.18 - Extensions to Generalized linear models/[FreeTutorials.Eu].url 129 Bytes24.2 - Sigmoid function Squashing/[FreeTutorials.Eu].url 129 Bytes24.3 - Mathematical formulation of Objective function/[FreeTutorials.Eu].url 129 Bytes24.4 - Weight vector/[FreeTutorials.Eu].url 129 Bytes24.5 - L2 Regularization Overfitting and Underfitting/[FreeTutorials.Eu].url 129 Bytes24.6 - L1 regularization and sparsity/[FreeTutorials.Eu].url 129 Bytes24.7 - Probabilistic Interpretation Gaussian Naive Bayes/[FreeTutorials.Eu].url 129 Bytes24.8 - Loss minimization interpretation/[FreeTutorials.Eu].url 129 Bytes24.9 - hyperparameters and random search/[FreeTutorials.Eu].url 129 Bytes25.1 - Geometric intuition of Linear Regression/[FreeTutorials.Eu].url 129 Bytes25.2 - Mathematical formulation/[FreeTutorials.Eu].url 129 Bytes25.3 - Real world Cases/[FreeTutorials.Eu].url 129 Bytes25.4 - Code sample for Linear Regression/[FreeTutorials.Eu].url 129 Bytes26.1 - Differentiation/[FreeTutorials.Eu].url 129 Bytes26.10 - Logistic regression formulation revisited/[FreeTutorials.Eu].url 129 Bytes26.11 - Why L1 regularization creates sparsity/[FreeTutorials.Eu].url 129 Bytes26.12 - Assignment 6 Implement SGD for linear regression/[FreeTutorials.Eu].url 129 Bytes26.13 - Revision questions/[FreeTutorials.Eu].url 129 Bytes26.2 - Online differentiation tools/[FreeTutorials.Eu].url 129 Bytes26.3 - Maxima and Minima/[FreeTutorials.Eu].url 129 Bytes26.4 - Vector calculus Grad/[FreeTutorials.Eu].url 129 Bytes26.5 - Gradient descent geometric intuition/[FreeTutorials.Eu].url 129 Bytes26.6 - Learning rate/[FreeTutorials.Eu].url 129 Bytes26.7 - Gradient descent for linear regression/[FreeTutorials.Eu].url 129 Bytes26.8 - SGD algorithm/[FreeTutorials.Eu].url 129 Bytes26.9 - Constrained Optimization & PCA/[FreeTutorials.Eu].url 129 Bytes27.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes28.1 - Geometric Intution/[FreeTutorials.Eu].url 129 Bytes28.10 - Train and run time complexities/[FreeTutorials.Eu].url 129 Bytes28.11 - nu-SVM control errors and support vectors/[FreeTutorials.Eu].url 129 Bytes28.12 - SVM Regression/[FreeTutorials.Eu].url 129 Bytes28.13 - Cases/[FreeTutorials.Eu].url 129 Bytes28.14 - Code Sample/[FreeTutorials.Eu].url 129 Bytes28.15 - Assignment-7 Apply SVM/[FreeTutorials.Eu].url 129 Bytes28.16 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes28.2 - Mathematical derivation/[FreeTutorials.Eu].url 129 Bytes28.3 - Why we take values +1 and and -1 for Support vector planes/[FreeTutorials.Eu].url 129 Bytes28.4 - Loss function (Hinge Loss) based interpretation/[FreeTutorials.Eu].url 129 Bytes28.5 - Dual form of SVM formulation/[FreeTutorials.Eu].url 129 Bytes28.6 - kernel trick/[FreeTutorials.Eu].url 129 Bytes28.7 - Polynomial Kernel/[FreeTutorials.Eu].url 129 Bytes28.8 - RBF-Kernel/[FreeTutorials.Eu].url 129 Bytes28.9 - Domain specific Kernels/[FreeTutorials.Eu].url 129 Bytes29.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes3.1 - Lists/[FreeTutorials.Eu].url 129 Bytes3.2 - Tuples part 1/[FreeTutorials.Eu].url 129 Bytes3.3 - Tuples part-2/[FreeTutorials.Eu].url 129 Bytes3.4 - Sets/[FreeTutorials.Eu].url 129 Bytes3.5 - Dictionary/[FreeTutorials.Eu].url 129 Bytes3.6 - Strings/[FreeTutorials.Eu].url 129 Bytes30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/[FreeTutorials.Eu].url 129 Bytes30.10 - Overfitting and Underfitting/[FreeTutorials.Eu].url 129 Bytes30.11 - Train and Run time complexity/[FreeTutorials.Eu].url 129 Bytes30.12 - Regression using Decision Trees/[FreeTutorials.Eu].url 129 Bytes30.13 - Cases/[FreeTutorials.Eu].url 129 Bytes30.14 - Code Samples/[FreeTutorials.Eu].url 129 Bytes30.15 - Assignment-8 Apply Decision Trees/[FreeTutorials.Eu].url 129 Bytes30.16 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes30.2 - Sample Decision tree/[FreeTutorials.Eu].url 129 Bytes30.3 - Building a decision TreeEntropy/[FreeTutorials.Eu].url 129 Bytes30.4 - Building a decision TreeInformation Gain/[FreeTutorials.Eu].url 129 Bytes30.5 - Building a decision Tree Gini Impurity/[FreeTutorials.Eu].url 129 Bytes30.6 - Building a decision Tree Constructing a DT/[FreeTutorials.Eu].url 129 Bytes30.7 - Building a decision Tree Splitting numerical features/[FreeTutorials.Eu].url 129 Bytes30.8 - Feature standardization/[FreeTutorials.Eu].url 129 Bytes30.9 - Building a decision TreeCategorical features with many possible values/[FreeTutorials.Eu].url 129 Bytes31.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes32.1 - What are ensembles/[FreeTutorials.Eu].url 129 Bytes32.10 - Residuals, Loss functions and gradients/[FreeTutorials.Eu].url 129 Bytes32.11 - Gradient Boosting/[FreeTutorials.Eu].url 129 Bytes32.12 - Regularization by Shrinkage/[FreeTutorials.Eu].url 129 Bytes32.13 - Train and Run time complexity/[FreeTutorials.Eu].url 129 Bytes32.14 - XGBoost Boosting + Randomization/[FreeTutorials.Eu].url 129 Bytes32.15 - AdaBoost geometric intuition/[FreeTutorials.Eu].url 129 Bytes32.16 - Stacking models/[FreeTutorials.Eu].url 129 Bytes32.17 - Cascading classifiers/[FreeTutorials.Eu].url 129 Bytes32.18 - Kaggle competitions vs Real world/[FreeTutorials.Eu].url 129 Bytes32.19 - Assignment-9 Apply Random Forests & GBDT/[FreeTutorials.Eu].url 129 Bytes32.2 - Bootstrapped Aggregation (Bagging) Intuition/[FreeTutorials.Eu].url 129 Bytes32.20 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes32.3 - Random Forest and their construction/[FreeTutorials.Eu].url 129 Bytes32.4 - Bias-Variance tradeoff/[FreeTutorials.Eu].url 129 Bytes32.5 - Train and run time complexity/[FreeTutorials.Eu].url 129 Bytes32.6 - BaggingCode Sample/[FreeTutorials.Eu].url 129 Bytes32.7 - Extremely randomized trees/[FreeTutorials.Eu].url 129 Bytes32.8 - Random Tree Cases/[FreeTutorials.Eu].url 129 Bytes32.9 - Boosting Intuition/[FreeTutorials.Eu].url 129 Bytes33.1 - Introduction/[FreeTutorials.Eu].url 129 Bytes33.10 - Indicator variables/[FreeTutorials.Eu].url 129 Bytes33.11 - Feature binning/[FreeTutorials.Eu].url 129 Bytes33.12 - Interaction variables/[FreeTutorials.Eu].url 129 Bytes33.13 - Mathematical transforms/[FreeTutorials.Eu].url 129 Bytes33.14 - Model specific featurizations/[FreeTutorials.Eu].url 129 Bytes33.15 - Feature orthogonality/[FreeTutorials.Eu].url 129 Bytes33.16 - Domain specific featurizations/[FreeTutorials.Eu].url 129 Bytes33.17 - Feature slicing/[FreeTutorials.Eu].url 129 Bytes33.18 - Kaggle Winners solutions/[FreeTutorials.Eu].url 129 Bytes33.2 - Moving window for Time Series Data/[FreeTutorials.Eu].url 129 Bytes33.3 - Fourier decomposition/[FreeTutorials.Eu].url 129 Bytes33.4 - Deep learning features LSTM/[FreeTutorials.Eu].url 129 Bytes33.5 - Image histogram/[FreeTutorials.Eu].url 129 Bytes33.6 - Keypoints SIFT/[FreeTutorials.Eu].url 129 Bytes33.7 - Deep learning features CNN/[FreeTutorials.Eu].url 129 Bytes33.8 - Relational data/[FreeTutorials.Eu].url 129 Bytes33.9 - Graph data/[FreeTutorials.Eu].url 129 Bytes34.1 - Calibration of ModelsNeed for calibration/[FreeTutorials.Eu].url 129 Bytes34.10 - AB testing/[FreeTutorials.Eu].url 129 Bytes34.11 - Data Science Life cycle/[FreeTutorials.Eu].url 129 Bytes34.12 - VC dimension/[FreeTutorials.Eu].url 129 Bytes34.2 - Productionization and deployment of Machine Learning Models/[FreeTutorials.Eu].url 129 Bytes34.3 - Calibration Plots/[FreeTutorials.Eu].url 129 Bytes34.4 - Platt’s CalibrationScaling/[FreeTutorials.Eu].url 129 Bytes34.5 - Isotonic Regression/[FreeTutorials.Eu].url 129 Bytes34.6 - Code Samples/[FreeTutorials.Eu].url 129 Bytes34.7 - Modeling in the presence of outliers RANSAC/[FreeTutorials.Eu].url 129 Bytes34.8 - Productionizing models/[FreeTutorials.Eu].url 129 Bytes34.9 - Retraining models periodically/[FreeTutorials.Eu].url 129 Bytes35.1 - What is Clustering/[FreeTutorials.Eu].url 129 Bytes35.10 - K-Medoids/[FreeTutorials.Eu].url 129 Bytes35.11 - Determining the right K/[FreeTutorials.Eu].url 129 Bytes35.12 - Code Samples/[FreeTutorials.Eu].url 129 Bytes35.13 - Time and space complexity/[FreeTutorials.Eu].url 129 Bytes35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeTutorials.Eu].url 129 Bytes35.2 - Unsupervised learning/[FreeTutorials.Eu].url 129 Bytes35.3 - Applications/[FreeTutorials.Eu].url 129 Bytes35.4 - Metrics for Clustering/[FreeTutorials.Eu].url 129 Bytes35.5 - K-Means Geometric intuition, Centroids/[FreeTutorials.Eu].url 129 Bytes35.6 - K-Means Mathematical formulation Objective function/[FreeTutorials.Eu].url 129 Bytes35.7 - K-Means Algorithm/[FreeTutorials.Eu].url 129 Bytes35.8 - How to initialize K-Means++/[FreeTutorials.Eu].url 129 Bytes35.9 - Failure casesLimitations/[FreeTutorials.Eu].url 129 Bytes36.1 - Agglomerative & Divisive, Dendrograms/[FreeTutorials.Eu].url 129 Bytes36.2 - Agglomerative Clustering/[FreeTutorials.Eu].url 129 Bytes36.3 - Proximity methods Advantages and Limitations/[FreeTutorials.Eu].url 129 Bytes36.4 - Time and Space Complexity/[FreeTutorials.Eu].url 129 Bytes36.5 - Limitations of Hierarchical Clustering/[FreeTutorials.Eu].url 129 Bytes36.6 - Code sample/[FreeTutorials.Eu].url 129 Bytes36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeTutorials.Eu].url 129 Bytes37.1 - Density based clustering/[FreeTutorials.Eu].url 129 Bytes37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/[FreeTutorials.Eu].url 129 Bytes37.11 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes37.2 - MinPts and Eps Density/[FreeTutorials.Eu].url 129 Bytes37.3 - Core, Border and Noise points/[FreeTutorials.Eu].url 129 Bytes37.4 - Density edge and Density connected points/[FreeTutorials.Eu].url 129 Bytes37.5 - DBSCAN Algorithm/[FreeTutorials.Eu].url 129 Bytes37.6 - Hyper Parameters MinPts and Eps/[FreeTutorials.Eu].url 129 Bytes37.7 - Advantages and Limitations of DBSCAN/[FreeTutorials.Eu].url 129 Bytes37.8 - Time and Space Complexity/[FreeTutorials.Eu].url 129 Bytes37.9 - Code samples/[FreeTutorials.Eu].url 129 Bytes38.1 - Problem formulation Movie reviews/[FreeTutorials.Eu].url 129 Bytes38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/[FreeTutorials.Eu].url 129 Bytes38.11 - Cold Start problem/[FreeTutorials.Eu].url 129 Bytes38.12 - Word vectors as MF/[FreeTutorials.Eu].url 129 Bytes38.13 - Eigen-Faces/[FreeTutorials.Eu].url 129 Bytes38.14 - Code example/[FreeTutorials.Eu].url 129 Bytes38.15 - Assignment-11 Apply Truncated SVD/[FreeTutorials.Eu].url 129 Bytes38.16 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes38.2 - Content based vs Collaborative Filtering/[FreeTutorials.Eu].url 129 Bytes38.3 - Similarity based Algorithms/[FreeTutorials.Eu].url 129 Bytes38.4 - Matrix Factorization PCA, SVD/[FreeTutorials.Eu].url 129 Bytes38.5 - Matrix Factorization NMF/[FreeTutorials.Eu].url 129 Bytes38.6 - Matrix Factorization for Collaborative filtering/[FreeTutorials.Eu].url 129 Bytes38.7 - Matrix Factorization for feature engineering/[FreeTutorials.Eu].url 129 Bytes38.8 - Clustering as MF/[FreeTutorials.Eu].url 129 Bytes38.9 - Hyperparameter tuning/[FreeTutorials.Eu].url 129 Bytes39.1 - Questions & Answers/[FreeTutorials.Eu].url 129 Bytes4.1 - Introduction/[FreeTutorials.Eu].url 129 Bytes4.10 - Debugging Python/[FreeTutorials.Eu].url 129 Bytes4.2 - Types of functions/[FreeTutorials.Eu].url 129 Bytes4.3 - Function arguments/[FreeTutorials.Eu].url 129 Bytes4.4 - Recursive functions/[FreeTutorials.Eu].url 129 Bytes4.5 - Lambda functions/[FreeTutorials.Eu].url 129 Bytes4.6 - Modules/[FreeTutorials.Eu].url 129 Bytes4.7 - Packages/[FreeTutorials.Eu].url 129 Bytes4.8 - File Handling/[FreeTutorials.Eu].url 129 Bytes4.9 - Exception Handling/[FreeTutorials.Eu].url 129 Bytes40.1 - BusinessReal world problem/[FreeTutorials.Eu].url 129 Bytes40.10 - Data Modeling Multi label Classification/[FreeTutorials.Eu].url 129 Bytes40.11 - Data preparation/[FreeTutorials.Eu].url 129 Bytes40.12 - Train-Test Split/[FreeTutorials.Eu].url 129 Bytes40.13 - Featurization/[FreeTutorials.Eu].url 129 Bytes40.14 - Logistic regression One VS Rest/[FreeTutorials.Eu].url 129 Bytes40.15 - Sampling data and tags+Weighted models/[FreeTutorials.Eu].url 129 Bytes40.16 - Logistic regression revisited/[FreeTutorials.Eu].url 129 Bytes40.17 - Why not use advanced techniques/[FreeTutorials.Eu].url 129 Bytes40.18 - Assignments/[FreeTutorials.Eu].url 129 Bytes40.2 - Business objectives and constraints/[FreeTutorials.Eu].url 129 Bytes40.3 - Mapping to an ML problem Data overview/[FreeTutorials.Eu].url 129 Bytes40.4 - Mapping to an ML problemML problem formulation/[FreeTutorials.Eu].url 129 Bytes40.5 - Mapping to an ML problemPerformance metrics/[FreeTutorials.Eu].url 129 Bytes40.6 - Hamming loss/[FreeTutorials.Eu].url 129 Bytes40.7 - EDAData Loading/[FreeTutorials.Eu].url 129 Bytes40.8 - EDAAnalysis of tags/[FreeTutorials.Eu].url 129 Bytes40.9 - EDAData Preprocessing/[FreeTutorials.Eu].url 129 Bytes41.1 - BusinessReal world problem Problem definition/[FreeTutorials.Eu].url 129 Bytes41.10 - EDA Feature analysis/[FreeTutorials.Eu].url 129 Bytes41.11 - EDA Data Visualization T-SNE/[FreeTutorials.Eu].url 129 Bytes41.12 - EDA TF-IDF weighted Word2Vec featurization/[FreeTutorials.Eu].url 129 Bytes41.13 - ML Models Loading Data/[FreeTutorials.Eu].url 129 Bytes41.14 - ML Models Random Model/[FreeTutorials.Eu].url 129 Bytes41.15 - ML Models Logistic Regression and Linear SVM/[FreeTutorials.Eu].url 129 Bytes41.16 - ML Models XGBoost/[FreeTutorials.Eu].url 129 Bytes41.17 - Assignments/[FreeTutorials.Eu].url 129 Bytes41.2 - Business objectives and constraints/[FreeTutorials.Eu].url 129 Bytes41.3 - Mapping to an ML problem Data overview/[FreeTutorials.Eu].url 129 Bytes41.4 - Mapping to an ML problem ML problem and performance metric/[FreeTutorials.Eu].url 129 Bytes41.5 - Mapping to an ML problem Train-test split/[FreeTutorials.Eu].url 129 Bytes41.6 - EDA Basic Statistics/[FreeTutorials.Eu].url 129 Bytes41.7 - EDA Basic Feature Extraction/[FreeTutorials.Eu].url 129 Bytes41.8 - EDA Text Preprocessing/[FreeTutorials.Eu].url 129 Bytes41.9 - EDA Advanced Feature Extraction/[FreeTutorials.Eu].url 129 Bytes42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/[FreeTutorials.Eu].url 129 Bytes42.10 - Text Pre-Processing Tokenization and Stop-word removal/[FreeTutorials.Eu].url 129 Bytes42.11 - Stemming/[FreeTutorials.Eu].url 129 Bytes42.12 - Text based product similarity Converting text to an n-D vector bag of words/[FreeTutorials.Eu].url 129 Bytes42.13 - Code for bag of words based product similarity/[FreeTutorials.Eu].url 129 Bytes42.14 - TF-IDF featurizing text based on word-importance/[FreeTutorials.Eu].url 129 Bytes42.15 - Code for TF-IDF based product similarity/[FreeTutorials.Eu].url 129 Bytes42.16 - Code for IDF based product similarity/[FreeTutorials.Eu].url 129 Bytes42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/[FreeTutorials.Eu].url 129 Bytes42.18 - Code for Average Word2Vec product similarity/[FreeTutorials.Eu].url 129 Bytes42.19 - TF-IDF weighted Word2Vec/[FreeTutorials.Eu].url 129 Bytes42.2 - Plan of action/[FreeTutorials.Eu].url 129 Bytes42.20 - Code for IDF weighted Word2Vec product similarity/[FreeTutorials.Eu].url 129 Bytes42.21 - Weighted similarity using brand and color/[FreeTutorials.Eu].url 129 Bytes42.22 - Code for weighted similarity/[FreeTutorials.Eu].url 129 Bytes42.23 - Building a real world solution/[FreeTutorials.Eu].url 129 Bytes42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/[FreeTutorials.Eu].url 129 Bytes42.25 - Using Keras + Tensorflow to extract features/[FreeTutorials.Eu].url 129 Bytes42.26 - Visual similarity based product similarity/[FreeTutorials.Eu].url 129 Bytes42.27 - Measuring goodness of our solution AB testing/[FreeTutorials.Eu].url 129 Bytes42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/[FreeTutorials.Eu].url 129 Bytes42.3 - Amazon product advertising API/[FreeTutorials.Eu].url 129 Bytes42.4 - Data folders and paths/[FreeTutorials.Eu].url 129 Bytes42.5 - Overview of the data and Terminology/[FreeTutorials.Eu].url 129 Bytes42.6 - Data cleaning and understandingMissing data in various features/[FreeTutorials.Eu].url 129 Bytes42.7 - Understand duplicate rows/[FreeTutorials.Eu].url 129 Bytes42.8 - Remove duplicates Part 1/[FreeTutorials.Eu].url 129 Bytes42.9 - Remove duplicates Part 2/[FreeTutorials.Eu].url 129 Bytes43.1 - Businessreal world problem Problem definition/[FreeTutorials.Eu].url 129 Bytes43.10 - ML models – using byte files only Random Model/[FreeTutorials.Eu].url 129 Bytes43.11 - k-NN/[FreeTutorials.Eu].url 129 Bytes43.12 - Logistic regression/[FreeTutorials.Eu].url 129 Bytes43.13 - Random Forest and Xgboost/[FreeTutorials.Eu].url 129 Bytes43.14 - ASM Files Feature extraction & Multiprocessing/[FreeTutorials.Eu].url 129 Bytes43.15 - File-size feature/[FreeTutorials.Eu].url 129 Bytes43.16 - Univariate analysis/[FreeTutorials.Eu].url 129 Bytes43.17 - t-SNE analysis/[FreeTutorials.Eu].url 129 Bytes43.18 - ML models on ASM file features/[FreeTutorials.Eu].url 129 Bytes43.19 - Models on all features t-SNE/[FreeTutorials.Eu].url 129 Bytes43.2 - Businessreal world problem Objectives and constraints/[FreeTutorials.Eu].url 129 Bytes43.20 - Models on all features RandomForest and Xgboost/[FreeTutorials.Eu].url 129 Bytes43.21 - Assignments/[FreeTutorials.Eu].url 129 Bytes43.3 - Machine Learning problem mapping Data overview/[FreeTutorials.Eu].url 129 Bytes43.4 - Machine Learning problem mapping ML problem/[FreeTutorials.Eu].url 129 Bytes43.5 - Machine Learning problem mapping Train and test splitting/[FreeTutorials.Eu].url 129 Bytes43.6 - Exploratory Data Analysis Class distribution/[FreeTutorials.Eu].url 129 Bytes43.7 - Exploratory Data Analysis Feature extraction from byte files/[FreeTutorials.Eu].url 129 Bytes43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/[FreeTutorials.Eu].url 129 Bytes43.9 - Exploratory Data Analysis Train-Test class distribution/[FreeTutorials.Eu].url 129 Bytes44.1 - BusinessReal world problemProblem definition/[FreeTutorials.Eu].url 129 Bytes44.10 - Exploratory Data AnalysisCold start problem/[FreeTutorials.Eu].url 129 Bytes44.11 - Computing Similarity matricesUser-User similarity matrix/[FreeTutorials.Eu].url 129 Bytes44.12 - Computing Similarity matricesMovie-Movie similarity/[FreeTutorials.Eu].url 129 Bytes44.13 - Computing Similarity matricesDoes movie-movie similarity work/[FreeTutorials.Eu].url 129 Bytes44.14 - ML ModelsSurprise library/[FreeTutorials.Eu].url 129 Bytes44.15 - Overview of the modelling strategy/[FreeTutorials.Eu].url 129 Bytes44.16 - Data Sampling/[FreeTutorials.Eu].url 129 Bytes44.17 - Google drive with intermediate files/[FreeTutorials.Eu].url 129 Bytes44.18 - Featurizations for regression/[FreeTutorials.Eu].url 129 Bytes44.19 - Data transformation for Surprise/[FreeTutorials.Eu].url 129 Bytes44.2 - Objectives and constraints/[FreeTutorials.Eu].url 129 Bytes44.20 - Xgboost with 13 features/[FreeTutorials.Eu].url 129 Bytes44.21 - Surprise Baseline model/[FreeTutorials.Eu].url 129 Bytes44.22 - Xgboost + 13 features +Surprise baseline model/[FreeTutorials.Eu].url 129 Bytes44.23 - Surprise KNN predictors/[FreeTutorials.Eu].url 129 Bytes44.24 - Matrix Factorization models using Surprise/[FreeTutorials.Eu].url 129 Bytes44.25 - SVD ++ with implicit feedback/[FreeTutorials.Eu].url 129 Bytes44.26 - Final models with all features and predictors/[FreeTutorials.Eu].url 129 Bytes44.27 - Comparison between various models/[FreeTutorials.Eu].url 129 Bytes44.28 - Assignments/[FreeTutorials.Eu].url 129 Bytes44.3 - Mapping to an ML problemData overview/[FreeTutorials.Eu].url 129 Bytes44.4 - Mapping to an ML problemML problem formulation/[FreeTutorials.Eu].url 129 Bytes44.5 - Exploratory Data AnalysisData preprocessing/[FreeTutorials.Eu].url 129 Bytes44.6 - Exploratory Data AnalysisTemporal Train-Test split/[FreeTutorials.Eu].url 129 Bytes44.7 - Exploratory Data AnalysisPreliminary data analysis/[FreeTutorials.Eu].url 129 Bytes44.8 - Exploratory Data AnalysisSparse matrix representation/[FreeTutorials.Eu].url 129 Bytes44.9 - Exploratory Data AnalysisAverage ratings for various slices/[FreeTutorials.Eu].url 129 Bytes45.1 - BusinessReal world problem Overview/[FreeTutorials.Eu].url 129 Bytes45.10 - Univariate AnalysisVariation Feature/[FreeTutorials.Eu].url 129 Bytes45.11 - Univariate AnalysisText feature/[FreeTutorials.Eu].url 129 Bytes45.12 - Machine Learning ModelsData preparation/[FreeTutorials.Eu].url 129 Bytes45.13 - Baseline Model Naive Bayes/[FreeTutorials.Eu].url 129 Bytes45.14 - K-Nearest Neighbors Classification/[FreeTutorials.Eu].url 129 Bytes45.15 - Logistic Regression with class balancing/[FreeTutorials.Eu].url 129 Bytes45.16 - Logistic Regression without class balancing/[FreeTutorials.Eu].url 129 Bytes45.17 - Linear-SVM/[FreeTutorials.Eu].url 129 Bytes45.18 - Random-Forest with one-hot encoded features/[FreeTutorials.Eu].url 129 Bytes45.19 - Random-Forest with response-coded features/[FreeTutorials.Eu].url 129 Bytes45.2 - Business objectives and constraints/[FreeTutorials.Eu].url 129 Bytes45.20 - Stacking Classifier/[FreeTutorials.Eu].url 129 Bytes45.21 - Majority Voting classifier/[FreeTutorials.Eu].url 129 Bytes45.22 - Assignments/[FreeTutorials.Eu].url 129 Bytes45.3 - ML problem formulation Data/[FreeTutorials.Eu].url 129 Bytes45.4 - ML problem formulation Mapping real world to ML problem#/[FreeTutorials.Eu].url 129 Bytes45.4 - ML problem formulation Mapping real world to ML problem/[FreeTutorials.Eu].url 129 Bytes45.5 - ML problem formulation Train, CV and Test data construction/[FreeTutorials.Eu].url 129 Bytes45.6 - Exploratory Data AnalysisReading data & preprocessing/[FreeTutorials.Eu].url 129 Bytes45.7 - Exploratory Data AnalysisDistribution of Class-labels/[FreeTutorials.Eu].url 129 Bytes45.8 - Exploratory Data Analysis “Random” Model/[FreeTutorials.Eu].url 129 Bytes45.9 - Univariate AnalysisGene feature/[FreeTutorials.Eu].url 129 Bytes46.1 - BusinessReal world problem Overview/[FreeTutorials.Eu].url 129 Bytes46.10 - Data Cleaning Speed/[FreeTutorials.Eu].url 129 Bytes46.11 - Data Cleaning Distance/[FreeTutorials.Eu].url 129 Bytes46.12 - Data Cleaning Fare/[FreeTutorials.Eu].url 129 Bytes46.13 - Data Cleaning Remove all outlierserroneous points/[FreeTutorials.Eu].url 129 Bytes46.14 - Data PreparationClusteringSegmentation/[FreeTutorials.Eu].url 129 Bytes46.15 - Data PreparationTime binning/[FreeTutorials.Eu].url 129 Bytes46.16 - Data PreparationSmoothing time-series data/[FreeTutorials.Eu].url 129 Bytes46.17 - Data PreparationSmoothing time-series data cont/[FreeTutorials.Eu].url 129 Bytes46.18 - Data Preparation Time series and Fourier transforms/[FreeTutorials.Eu].url 129 Bytes46.19 - Ratios and previous-time-bin values/[FreeTutorials.Eu].url 129 Bytes46.2 - Objectives and Constraints/[FreeTutorials.Eu].url 129 Bytes46.20 - Simple moving average/[FreeTutorials.Eu].url 129 Bytes46.21 - Weighted Moving average/[FreeTutorials.Eu].url 129 Bytes46.22 - Exponential weighted moving average/[FreeTutorials.Eu].url 129 Bytes46.23 - Results/[FreeTutorials.Eu].url 129 Bytes46.24 - Regression models Train-Test split & Features/[FreeTutorials.Eu].url 129 Bytes46.25 - Linear regression/[FreeTutorials.Eu].url 129 Bytes46.26 - Random Forest regression/[FreeTutorials.Eu].url 129 Bytes46.27 - Xgboost Regression/[FreeTutorials.Eu].url 129 Bytes46.28 - Model comparison/[FreeTutorials.Eu].url 129 Bytes46.29 - Assignment/[FreeTutorials.Eu].url 129 Bytes46.3 - Mapping to ML problem Data/[FreeTutorials.Eu].url 129 Bytes46.4 - Mapping to ML problem dask dataframes/[FreeTutorials.Eu].url 129 Bytes46.5 - Mapping to ML problem FieldsFeatures/[FreeTutorials.Eu].url 129 Bytes46.6 - Mapping to ML problem Time series forecastingRegression/[FreeTutorials.Eu].url 129 Bytes46.7 - Mapping to ML problem Performance metrics/[FreeTutorials.Eu].url 129 Bytes46.8 - Data Cleaning Latitude and Longitude data/[FreeTutorials.Eu].url 129 Bytes46.9 - Data Cleaning Trip Duration/[FreeTutorials.Eu].url 129 Bytes47.1 - History of Neural networks and Deep Learning/[FreeTutorials.Eu].url 129 Bytes47.10 - Backpropagation/[FreeTutorials.Eu].url 129 Bytes47.11 - Activation functions/[FreeTutorials.Eu].url 129 Bytes47.12 - Vanishing Gradient problem/[FreeTutorials.Eu].url 129 Bytes47.13 - Bias-Variance tradeoff/[FreeTutorials.Eu].url 129 Bytes47.14 - Decision surfaces Playground/[FreeTutorials.Eu].url 129 Bytes47.2 - How Biological Neurons work/[FreeTutorials.Eu].url 129 Bytes47.3 - Growth of biological neural networks/[FreeTutorials.Eu].url 129 Bytes47.4 - Diagrammatic representation Logistic Regression and Perceptron/[FreeTutorials.Eu].url 129 Bytes47.5 - Multi-Layered Perceptron (MLP)/[FreeTutorials.Eu].url 129 Bytes47.6 - Notation/[FreeTutorials.Eu].url 129 Bytes47.7 - Training a single-neuron model/[FreeTutorials.Eu].url 129 Bytes47.8 - Training an MLP Chain Rule/[FreeTutorials.Eu].url 129 Bytes47.9 - Training an MLPMemoization/[FreeTutorials.Eu].url 129 Bytes48.1 - Deep Multi-layer perceptrons1980s to 2010s/[FreeTutorials.Eu].url 129 Bytes48.10 - Nesterov Accelerated Gradient (NAG)/[FreeTutorials.Eu].url 129 Bytes48.11 - OptimizersAdaGrad/[FreeTutorials.Eu].url 129 Bytes48.12 - Optimizers Adadelta andRMSProp/[FreeTutorials.Eu].url 129 Bytes48.13 - Adam/[FreeTutorials.Eu].url 129 Bytes48.14 - Which algorithm to choose when/[FreeTutorials.Eu].url 129 Bytes48.15 - Gradient Checking and clipping/[FreeTutorials.Eu].url 129 Bytes48.16 - Softmax and Cross-entropy for multi-class classification/[FreeTutorials.Eu].url 129 Bytes48.17 - How to train a Deep MLP/[FreeTutorials.Eu].url 129 Bytes48.18 - Auto Encoders/[FreeTutorials.Eu].url 129 Bytes48.19 - Word2Vec CBOW/[FreeTutorials.Eu].url 129 Bytes48.2 - Dropout layers & Regularization/[FreeTutorials.Eu].url 129 Bytes48.20 - Word2Vec Skip-gram/[FreeTutorials.Eu].url 129 Bytes48.21 - Word2Vec Algorithmic Optimizations/[FreeTutorials.Eu].url 129 Bytes48.3 - Rectified Linear Units (ReLU)/[FreeTutorials.Eu].url 129 Bytes48.4 - Weight initialization/[FreeTutorials.Eu].url 129 Bytes48.5 - Batch Normalization/[FreeTutorials.Eu].url 129 Bytes48.6 - OptimizersHill-descent analogy in 2D/[FreeTutorials.Eu].url 129 Bytes48.7 - OptimizersHill descent in 3D and contours/[FreeTutorials.Eu].url 129 Bytes48.8 - SGD Recap/[FreeTutorials.Eu].url 129 Bytes48.9 - Batch SGD with momentum/[FreeTutorials.Eu].url 129 Bytes49.1 - Tensorflow and Keras overview/[FreeTutorials.Eu].url 129 Bytes49.10 - Model 3 Batch Normalization/[FreeTutorials.Eu].url 129 Bytes49.11 - Model 4 Dropout/[FreeTutorials.Eu].url 129 Bytes49.12 - MNIST classification in Keras/[FreeTutorials.Eu].url 129 Bytes49.13 - Hyperparameter tuning in Keras/[FreeTutorials.Eu].url 129 Bytes49.14 - Exercise Try different MLP architectures on MNIST dataset/[FreeTutorials.Eu].url 129 Bytes49.2 - GPU vs CPU for Deep Learning/[FreeTutorials.Eu].url 129 Bytes49.3 - Google Colaboratory/[FreeTutorials.Eu].url 129 Bytes49.4 - Install TensorFlow/[FreeTutorials.Eu].url 129 Bytes49.5 - Online documentation and tutorials/[FreeTutorials.Eu].url 129 Bytes49.6 - Softmax Classifier on MNIST dataset/[FreeTutorials.Eu].url 129 Bytes49.7 - MLP Initialization/[FreeTutorials.Eu].url 129 Bytes49.8 - Model 1 Sigmoid activation/[FreeTutorials.Eu].url 129 Bytes49.9 - Model 2 ReLU activation/[FreeTutorials.Eu].url 129 Bytes5.1 - Numpy Introduction/[FreeTutorials.Eu].url 129 Bytes5.2 - Numerical operations on Numpy/[FreeTutorials.Eu].url 129 Bytes50.1 - Biological inspiration Visual Cortex/[FreeTutorials.Eu].url 129 Bytes50.10 - Data Augmentation/[FreeTutorials.Eu].url 129 Bytes50.11 - Convolution Layers in Keras/[FreeTutorials.Eu].url 129 Bytes50.12 - AlexNet/[FreeTutorials.Eu].url 129 Bytes50.13 - VGGNet/[FreeTutorials.Eu].url 129 Bytes50.14 - Residual Network/[FreeTutorials.Eu].url 129 Bytes50.15 - Inception Network/[FreeTutorials.Eu].url 129 Bytes50.16 - What is Transfer learning/[FreeTutorials.Eu].url 129 Bytes50.17 - Code example Cats vs Dogs/[FreeTutorials.Eu].url 129 Bytes50.18 - Code Example MNIST dataset/[FreeTutorials.Eu].url 129 Bytes50.19 - Assignment Try various CNN networks on MNIST dataset#/[FreeTutorials.Eu].url 129 Bytes50.2 - ConvolutionEdge Detection on images/[FreeTutorials.Eu].url 129 Bytes50.3 - ConvolutionPadding and strides/[FreeTutorials.Eu].url 129 Bytes50.4 - Convolution over RGB images/[FreeTutorials.Eu].url 129 Bytes50.5 - Convolutional layer/[FreeTutorials.Eu].url 129 Bytes50.6 - Max-pooling/[FreeTutorials.Eu].url 129 Bytes50.7 - CNN Training Optimization/[FreeTutorials.Eu].url 129 Bytes50.8 - Example CNN LeNet [1998]/[FreeTutorials.Eu].url 129 Bytes50.9 - ImageNet dataset/[FreeTutorials.Eu].url 129 Bytes51.1 - Why RNNs/[FreeTutorials.Eu].url 129 Bytes51.10 - Code example IMDB Sentiment classification/[FreeTutorials.Eu].url 129 Bytes51.11 - Exercise Amazon Fine Food reviews LSTM model/[FreeTutorials.Eu].url 129 Bytes51.2 - Recurrent Neural Network/[FreeTutorials.Eu].url 129 Bytes51.3 - Training RNNs Backprop/[FreeTutorials.Eu].url 129 Bytes51.4 - Types of RNNs/[FreeTutorials.Eu].url 129 Bytes51.5 - Need for LSTMGRU/[FreeTutorials.Eu].url 129 Bytes51.6 - LSTM/[FreeTutorials.Eu].url 129 Bytes51.7 - GRUs/[FreeTutorials.Eu].url 129 Bytes51.8 - Deep RNN/[FreeTutorials.Eu].url 129 Bytes51.9 - Bidirectional RNN/[FreeTutorials.Eu].url 129 Bytes52.1 - Questions and Answers/[FreeTutorials.Eu].url 129 Bytes53.1 - Self Driving Car Problem definition/[FreeTutorials.Eu].url 129 Bytes53.10 - NVIDIA’s end to end CNN model/[FreeTutorials.Eu].url 129 Bytes53.11 - Train the model/[FreeTutorials.Eu].url 129 Bytes53.12 - Test and visualize the output/[FreeTutorials.Eu].url 129 Bytes53.13 - Extensions/[FreeTutorials.Eu].url 129 Bytes53.14 - Assignment/[FreeTutorials.Eu].url 129 Bytes53.2 - Datasets#/[FreeTutorials.Eu].url 129 Bytes53.2 - Datasets/[FreeTutorials.Eu].url 129 Bytes53.3 - Data understanding & Analysis Files and folders/[FreeTutorials.Eu].url 129 Bytes53.4 - Dash-cam images and steering angles/[FreeTutorials.Eu].url 129 Bytes53.5 - Split the dataset Train vs Test/[FreeTutorials.Eu].url 129 Bytes53.6 - EDA Steering angles/[FreeTutorials.Eu].url 129 Bytes53.7 - Mean Baseline model simple/[FreeTutorials.Eu].url 129 Bytes53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/[FreeTutorials.Eu].url 129 Bytes53.9 - Batch load the dataset/[FreeTutorials.Eu].url 129 Bytes54.1 - Real-world problem/[FreeTutorials.Eu].url 129 Bytes54.10 - MIDI music generation/[FreeTutorials.Eu].url 129 Bytes54.11 - Survey blog/[FreeTutorials.Eu].url 129 Bytes54.2 - Music representation/[FreeTutorials.Eu].url 129 Bytes54.3 - Char-RNN with abc-notation Char-RNN model/[FreeTutorials.Eu].url 129 Bytes54.4 - Char-RNN with abc-notation Data preparation/[FreeTutorials.Eu].url 129 Bytes54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/[FreeTutorials.Eu].url 129 Bytes54.6 - Char-RNN with abc-notation State full RNN/[FreeTutorials.Eu].url 129 Bytes54.7 - Char-RNN with abc-notation Model architecture,Model training/[FreeTutorials.Eu].url 129 Bytes54.8 - Char-RNN with abc-notation Music generation/[FreeTutorials.Eu].url 129 Bytes54.9 - Char-RNN with abc-notation Generate tabla music/[FreeTutorials.Eu].url 129 Bytes55.1 - Human Activity Recognition Problem definition/[FreeTutorials.Eu].url 129 Bytes55.2 - Dataset understanding/[FreeTutorials.Eu].url 129 Bytes55.3 - Data cleaning & preprocessing/[FreeTutorials.Eu].url 129 Bytes55.4 - EDAUnivariate analysis/[FreeTutorials.Eu].url 129 Bytes55.5 - EDAData visualization using t-SNE/[FreeTutorials.Eu].url 129 Bytes55.6 - Classical ML models/[FreeTutorials.Eu].url 129 Bytes55.7 - Deep-learning Model/[FreeTutorials.Eu].url 129 Bytes55.8 - Exercise Build deeper LSTM models and hyper-param tune them/[FreeTutorials.Eu].url 129 Bytes56.1 - Problem definition/[FreeTutorials.Eu].url 129 Bytes56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/[FreeTutorials.Eu].url 129 Bytes56.11 - PageRank/[FreeTutorials.Eu].url 129 Bytes56.12 - Shortest Path/[FreeTutorials.Eu].url 129 Bytes56.13 - Connected-components/[FreeTutorials.Eu].url 129 Bytes56.14 - Adar Index/[FreeTutorials.Eu].url 129 Bytes56.15 - Kartz Centrality/[FreeTutorials.Eu].url 129 Bytes56.16 - HITS Score/[FreeTutorials.Eu].url 129 Bytes56.17 - SVD/[FreeTutorials.Eu].url 129 Bytes56.18 - Weight features/[FreeTutorials.Eu].url 129 Bytes56.19 - Modeling/[FreeTutorials.Eu].url 129 Bytes56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/[FreeTutorials.Eu].url 129 Bytes56.3 - Data format & Limitations/[FreeTutorials.Eu].url 129 Bytes56.4 - Mapping to a supervised classification problem/[FreeTutorials.Eu].url 129 Bytes56.5 - Business constraints & Metrics/[FreeTutorials.Eu].url 129 Bytes56.6 - EDABasic Stats/[FreeTutorials.Eu].url 129 Bytes56.7 - EDAFollower and following stats/[FreeTutorials.Eu].url 129 Bytes56.8 - EDABinary Classification Task/[FreeTutorials.Eu].url 129 Bytes56.9 - EDATrain and test split/[FreeTutorials.Eu].url 129 Bytes57.1 - Introduction to Databases/[FreeTutorials.Eu].url 129 Bytes57.10 - ORDER BY/[FreeTutorials.Eu].url 129 Bytes57.11 - DISTINCT/[FreeTutorials.Eu].url 129 Bytes57.12 - WHERE, Comparison operators, NULL/[FreeTutorials.Eu].url 129 Bytes57.13 - Logical Operators/[FreeTutorials.Eu].url 129 Bytes57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/[FreeTutorials.Eu].url 129 Bytes57.15 - GROUP BY/[FreeTutorials.Eu].url 129 Bytes57.16 - HAVING/[FreeTutorials.Eu].url 129 Bytes57.17 - Order of keywords#/[FreeTutorials.Eu].url 129 Bytes57.18 - Join and Natural Join/[FreeTutorials.Eu].url 129 Bytes57.19 - Inner, Left, Right and Outer joins/[FreeTutorials.Eu].url 129 Bytes57.2 - Why SQL/[FreeTutorials.Eu].url 129 Bytes57.20 - Sub QueriesNested QueriesInner Queries/[FreeTutorials.Eu].url 129 Bytes57.21 - DMLINSERT/[FreeTutorials.Eu].url 129 Bytes57.22 - DMLUPDATE , DELETE/[FreeTutorials.Eu].url 129 Bytes57.23 - DDLCREATE TABLE/[FreeTutorials.Eu].url 129 Bytes57.24 - DDLALTER ADD, MODIFY, DROP/[FreeTutorials.Eu].url 129 Bytes57.25 - DDLDROP TABLE, TRUNCATE, DELETE/[FreeTutorials.Eu].url 129 Bytes57.26 - Data Control Language GRANT, REVOKE/[FreeTutorials.Eu].url 129 Bytes57.27 - Learning resources/[FreeTutorials.Eu].url 129 Bytes57.3 - Execution of an SQL statement/[FreeTutorials.Eu].url 129 Bytes57.4 - IMDB dataset/[FreeTutorials.Eu].url 129 Bytes57.5 - Installing MySQL/[FreeTutorials.Eu].url 129 Bytes57.6 - Load IMDB data/[FreeTutorials.Eu].url 129 Bytes57.7 - USE, DESCRIBE, SHOW TABLES/[FreeTutorials.Eu].url 129 Bytes57.8 - SELECT/[FreeTutorials.Eu].url 129 Bytes57.9 - LIMIT, OFFSET/[FreeTutorials.Eu].url 129 Bytes58.1 - AD-Click Predicition/[FreeTutorials.Eu].url 129 Bytes59.1 - Revision Questions/[FreeTutorials.Eu].url 129 Bytes59.2 - Questions/[FreeTutorials.Eu].url 129 Bytes59.3 - External resources for Interview Questions/[FreeTutorials.Eu].url 129 Bytes6.1 - Getting started with Matplotlib/[FreeTutorials.Eu].url 129 Bytes7.1 - Getting started with pandas/[FreeTutorials.Eu].url 129 Bytes7.2 - Data Frame Basics/[FreeTutorials.Eu].url 129 Bytes7.3 - Key Operations on Data Frames/[FreeTutorials.Eu].url 129 Bytes8.1 - Space and Time Complexity Find largest number in a list/[FreeTutorials.Eu].url 129 Bytes8.2 - Binary search/[FreeTutorials.Eu].url 129 Bytes8.3 - Find elements common in two lists/[FreeTutorials.Eu].url 129 Bytes8.4 - Find elements common in two lists using a HashtableDict/[FreeTutorials.Eu].url 129 Bytes9.1 - Introduction to IRIS dataset and 2D scatter plot/[FreeTutorials.Eu].url 129 Bytes9.10 - Percentiles and Quantiles/[FreeTutorials.Eu].url 129 Bytes9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/[FreeTutorials.Eu].url 129 Bytes9.12 - Box-plot with Whiskers/[FreeTutorials.Eu].url 129 Bytes9.13 - Violin Plots/[FreeTutorials.Eu].url 129 Bytes9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/[FreeTutorials.Eu].url 129 Bytes9.15 - Multivariate Probability Density, Contour Plot/[FreeTutorials.Eu].url 129 Bytes9.16 - Exercise Perform EDA on Haberman dataset/[FreeTutorials.Eu].url 129 Bytes9.2 - 3D scatter plot/[FreeTutorials.Eu].url 129 Bytes9.3 - Pair plots/[FreeTutorials.Eu].url 129 Bytes9.4 - Limitations of Pair Plots/[FreeTutorials.Eu].url 129 Bytes9.5 - Histogram and Introduction to PDF(Probability Density Function)/[FreeTutorials.Eu].url 129 Bytes9.6 - Univariate Analysis using PDF/[FreeTutorials.Eu].url 129 Bytes9.7 - CDF(Cumulative Distribution Function)/[FreeTutorials.Eu].url 129 Bytes9.8 - Mean, Variance and Standard Deviation/[FreeTutorials.Eu].url 129 Bytes9.9 - Median/[FreeTutorials.Eu].url 129 Bytes[FreeCourseSite.com].url 127 Bytes[CourseClub.ME].url 122 Bytes1.1 - How to Learn from Appliedaicourse/FTUApps.com website coming soon.txt 94 Bytes1.2 - How the Job Guarantee program works/FTUApps.com website coming soon.txt 94 Bytes10.1 - Why learn it/FTUApps.com website coming soon.txt 94 Bytes10.10 - Hyper Cube,Hyper Cuboid/FTUApps.com website coming soon.txt 94 Bytes10.11 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/FTUApps.com website coming soon.txt 94 Bytes10.3 - Dot Product and Angle between 2 Vectors/FTUApps.com website coming soon.txt 94 Bytes10.4 - Projection and Unit Vector/FTUApps.com website coming soon.txt 94 Bytes10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/FTUApps.com website coming soon.txt 94 Bytes10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/FTUApps.com website coming soon.txt 94 Bytes10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/FTUApps.com website coming soon.txt 94 Bytes10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/FTUApps.com website coming soon.txt 94 Bytes10.9 - Square ,Rectangle/FTUApps.com website coming soon.txt 94 Bytes11.1 - Introduction to Probability and Statistics/FTUApps.com website coming soon.txt 94 Bytes11.10 - How distributions are used/FTUApps.com website coming soon.txt 94 Bytes11.11 - Chebyshev’s inequality/FTUApps.com website coming soon.txt 94 Bytes11.12 - Discrete and Continuous Uniform distributions/FTUApps.com website coming soon.txt 94 Bytes11.13 - How to randomly sample data points (Uniform Distribution)/FTUApps.com website coming soon.txt 94 Bytes11.14 - Bernoulli and Binomial Distribution/FTUApps.com website coming soon.txt 94 Bytes11.15 - Log Normal Distribution/FTUApps.com website coming soon.txt 94 Bytes11.16 - Power law distribution/FTUApps.com website coming soon.txt 94 Bytes11.17 - Box cox transform/FTUApps.com website coming soon.txt 94 Bytes11.18 - Applications of non-gaussian distributions/FTUApps.com website coming soon.txt 94 Bytes11.19 - Co-variance/FTUApps.com website coming soon.txt 94 Bytes11.2 - Population and Sample/FTUApps.com website coming soon.txt 94 Bytes11.20 - Pearson Correlation Coefficient/FTUApps.com website coming soon.txt 94 Bytes11.21 - Spearman Rank Correlation Coefficient/FTUApps.com website coming soon.txt 94 Bytes11.22 - Correlation vs Causation/FTUApps.com website coming soon.txt 94 Bytes11.23 - How to use correlations/FTUApps.com website coming soon.txt 94 Bytes11.24 - Confidence interval (C.I) Introduction/FTUApps.com website coming soon.txt 94 Bytes11.25 - Computing confidence interval given the underlying distribution/FTUApps.com website coming soon.txt 94 Bytes11.26 - C.I for mean of a normal random variable/FTUApps.com website coming soon.txt 94 Bytes11.27 - Confidence interval using bootstrapping/FTUApps.com website coming soon.txt 94 Bytes11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/FTUApps.com website coming soon.txt 94 Bytes11.29 - Hypothesis Testing Intution with coin toss example/FTUApps.com website coming soon.txt 94 Bytes11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/FTUApps.com website coming soon.txt 94 Bytes11.30 - Resampling and permutation test/FTUApps.com website coming soon.txt 94 Bytes11.31 - K-S Test for similarity of two distributions/FTUApps.com website coming soon.txt 94 Bytes11.32 - Code Snippet K-S Test/FTUApps.com website coming soon.txt 94 Bytes11.33 - Hypothesis testing another example/FTUApps.com website coming soon.txt 94 Bytes11.34 - Resampling and Permutation test another example/FTUApps.com website coming soon.txt 94 Bytes11.35 - How to use hypothesis testing/FTUApps.com website coming soon.txt 94 Bytes11.36 - Proportional Sampling/FTUApps.com website coming soon.txt 94 Bytes11.37 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/FTUApps.com website coming soon.txt 94 Bytes11.5 - Symmetric distribution, Skewness and Kurtosis/FTUApps.com website coming soon.txt 94 Bytes11.6 - Standard normal variate (Z) and standardization/FTUApps.com website coming soon.txt 94 Bytes11.7 - Kernel density estimation/FTUApps.com website coming soon.txt 94 Bytes11.8 - Sampling distribution & Central Limit theorem/FTUApps.com website coming soon.txt 94 Bytes11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/FTUApps.com website coming soon.txt 94 Bytes12.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes13.1 - What is Dimensionality reduction/FTUApps.com website coming soon.txt 94 Bytes13.10 - Code to Load MNIST Data Set/FTUApps.com website coming soon.txt 94 Bytes13.2 - Row Vector and Column Vector/FTUApps.com website coming soon.txt 94 Bytes13.3 - How to represent a data set/FTUApps.com website coming soon.txt 94 Bytes13.4 - How to represent a dataset as a Matrix/FTUApps.com website coming soon.txt 94 Bytes13.5 - Data Preprocessing Feature Normalisation/FTUApps.com website coming soon.txt 94 Bytes13.6 - Mean of a data matrix/FTUApps.com website coming soon.txt 94 Bytes13.7 - Data Preprocessing Column Standardization/FTUApps.com website coming soon.txt 94 Bytes13.8 - Co-variance of a Data Matrix/FTUApps.com website coming soon.txt 94 Bytes13.9 - MNIST dataset (784 dimensional)/FTUApps.com website coming soon.txt 94 Bytes14.1 - Why learn PCA/FTUApps.com website coming soon.txt 94 Bytes14.10 - PCA for dimensionality reduction (not-visualization)/FTUApps.com website coming soon.txt 94 Bytes14.2 - Geometric intuition of PCA/FTUApps.com website coming soon.txt 94 Bytes14.3 - Mathematical objective function of PCA/FTUApps.com website coming soon.txt 94 Bytes14.4 - Alternative formulation of PCA Distance minimization/FTUApps.com website coming soon.txt 94 Bytes14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/FTUApps.com website coming soon.txt 94 Bytes14.6 - PCA for Dimensionality Reduction and Visualization/FTUApps.com website coming soon.txt 94 Bytes14.7 - Visualize MNIST dataset/FTUApps.com website coming soon.txt 94 Bytes14.8 - Limitations of PCA/FTUApps.com website coming soon.txt 94 Bytes14.9 - PCA Code example/FTUApps.com website coming soon.txt 94 Bytes15.1 - What is t-SNE/FTUApps.com website coming soon.txt 94 Bytes15.2 - Neighborhood of a point, Embedding/FTUApps.com website coming soon.txt 94 Bytes15.3 - Geometric intuition of t-SNE/FTUApps.com website coming soon.txt 94 Bytes15.4 - Crowding Problem/FTUApps.com website coming soon.txt 94 Bytes15.5 - How to apply t-SNE and interpret its output/FTUApps.com website coming soon.txt 94 Bytes15.6 - t-SNE on MNIST/FTUApps.com website coming soon.txt 94 Bytes15.7 - Code example of t-SNE/FTUApps.com website coming soon.txt 94 Bytes15.8 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes16.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes17.1 - Dataset overview Amazon Fine Food reviews(EDA)/FTUApps.com website coming soon.txt 94 Bytes17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec/FTUApps.com website coming soon.txt 94 Bytes17.11 - Bag of Words( Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.12 - Text Preprocessing( Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.13 - Bi-Grams and n-grams (Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.14 - TF-IDF (Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.15 - Word2Vec (Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)/FTUApps.com website coming soon.txt 94 Bytes17.17 - Assignment-2 Apply t-SNE/FTUApps.com website coming soon.txt 94 Bytes17.2 - Data Cleaning Deduplication/FTUApps.com website coming soon.txt 94 Bytes17.3 - Why convert text to a vector/FTUApps.com website coming soon.txt 94 Bytes17.4 - Bag of Words (BoW)/FTUApps.com website coming soon.txt 94 Bytes17.5 - Text Preprocessing Stemming/FTUApps.com website coming soon.txt 94 Bytes17.6 - uni-gram, bi-gram, n-grams/FTUApps.com website coming soon.txt 94 Bytes17.7 - tf-idf (term frequency- inverse document frequency)/FTUApps.com website coming soon.txt 94 Bytes17.8 - Why use log in IDF/FTUApps.com website coming soon.txt 94 Bytes17.9 - Word2Vec/FTUApps.com website coming soon.txt 94 Bytes18.1 - How “Classification” works/FTUApps.com website coming soon.txt 94 Bytes18.10 - KNN Limitations/FTUApps.com website coming soon.txt 94 Bytes18.11 - Decision surface for K-NN as K changes/FTUApps.com website coming soon.txt 94 Bytes18.12 - Overfitting and Underfitting/FTUApps.com website coming soon.txt 94 Bytes18.13 - Need for Cross validation/FTUApps.com website coming soon.txt 94 Bytes18.14 - K-fold cross validation/FTUApps.com website coming soon.txt 94 Bytes18.15 - Visualizing train, validation and test datasets/FTUApps.com website coming soon.txt 94 Bytes18.16 - How to determine overfitting and underfitting/FTUApps.com website coming soon.txt 94 Bytes18.17 - Time based splitting/FTUApps.com website coming soon.txt 94 Bytes18.18 - k-NN for regression/FTUApps.com website coming soon.txt 94 Bytes18.19 - Weighted k-NN/FTUApps.com website coming soon.txt 94 Bytes18.2 - Data matrix notation/FTUApps.com website coming soon.txt 94 Bytes18.20 - Voronoi diagram/FTUApps.com website coming soon.txt 94 Bytes18.21 - Binary search tree/FTUApps.com website coming soon.txt 94 Bytes18.22 - How to build a kd-tree/FTUApps.com website coming soon.txt 94 Bytes18.23 - Find nearest neighbours using kd-tree/FTUApps.com website coming soon.txt 94 Bytes18.24 - Limitations of Kd tree/FTUApps.com website coming soon.txt 94 Bytes18.25 - Extensions/FTUApps.com website coming soon.txt 94 Bytes18.26 - Hashing vs LSH/FTUApps.com website coming soon.txt 94 Bytes18.27 - LSH for cosine similarity/FTUApps.com website coming soon.txt 94 Bytes18.28 - LSH for euclidean distance/FTUApps.com website coming soon.txt 94 Bytes18.29 - Probabilistic class label/FTUApps.com website coming soon.txt 94 Bytes18.3 - Classification vs Regression (examples)/FTUApps.com website coming soon.txt 94 Bytes18.30 - Code SampleDecision boundary/FTUApps.com website coming soon.txt 94 Bytes18.31 - Code SampleCross Validation/FTUApps.com website coming soon.txt 94 Bytes18.32 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes18.4 - K-Nearest Neighbours Geometric intuition with a toy example/FTUApps.com website coming soon.txt 94 Bytes18.5 - Failure cases of KNN/FTUApps.com website coming soon.txt 94 Bytes18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming/FTUApps.com website coming soon.txt 94 Bytes18.7 - Cosine Distance & Cosine Similarity/FTUApps.com website coming soon.txt 94 Bytes18.8 - How to measure the effectiveness of k-NN/FTUApps.com website coming soon.txt 94 Bytes18.9 - TestEvaluation time and space complexity/FTUApps.com website coming soon.txt 94 Bytes19.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes2.1 - Python, Anaconda and relevant packages installations/FTUApps.com website coming soon.txt 94 Bytes2.10 - Control flow for loop/FTUApps.com website coming soon.txt 94 Bytes2.11 - Control flow break and continue/FTUApps.com website coming soon.txt 94 Bytes2.2 - Why learn Python/FTUApps.com website coming soon.txt 94 Bytes2.3 - Keywords and identifiers/FTUApps.com website coming soon.txt 94 Bytes2.4 - comments, indentation and statements/FTUApps.com website coming soon.txt 94 Bytes2.5 - Variables and data types in Python/FTUApps.com website coming soon.txt 94 Bytes2.6 - Standard Input and Output/FTUApps.com website coming soon.txt 94 Bytes2.7 - Operators/FTUApps.com website coming soon.txt 94 Bytes2.8 - Control flow if else/FTUApps.com website coming soon.txt 94 Bytes2.9 - Control flow while loop/FTUApps.com website coming soon.txt 94 Bytes20.1 - Introduction/FTUApps.com website coming soon.txt 94 Bytes20.10 - Local reachability-density(A)/FTUApps.com website coming soon.txt 94 Bytes20.11 - Local outlier Factor(A)/FTUApps.com website coming soon.txt 94 Bytes20.12 - Impact of Scale & Column standardization/FTUApps.com website coming soon.txt 94 Bytes20.13 - Interpretability/FTUApps.com website coming soon.txt 94 Bytes20.14 - Feature Importance and Forward Feature selection/FTUApps.com website coming soon.txt 94 Bytes20.15 - Handling categorical and numerical features/FTUApps.com website coming soon.txt 94 Bytes20.16 - Handling missing values by imputation/FTUApps.com website coming soon.txt 94 Bytes20.17 - curse of dimensionality/FTUApps.com website coming soon.txt 94 Bytes20.18 - Bias-Variance tradeoff/FTUApps.com website coming soon.txt 94 Bytes20.19 - Intuitive understanding of bias-variance/FTUApps.com website coming soon.txt 94 Bytes20.2 - Imbalanced vs balanced dataset/FTUApps.com website coming soon.txt 94 Bytes20.20 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes20.21 - best and wrost case of algorithm/FTUApps.com website coming soon.txt 94 Bytes20.3 - Multi-class classification/FTUApps.com website coming soon.txt 94 Bytes20.4 - k-NN, given a distance or similarity matrix/FTUApps.com website coming soon.txt 94 Bytes20.5 - Train and test set differences/FTUApps.com website coming soon.txt 94 Bytes20.6 - Impact of outliers/FTUApps.com website coming soon.txt 94 Bytes20.7 - Local outlier Factor (Simple solution Mean distance to Knn)/FTUApps.com website coming soon.txt 94 Bytes20.8 - k distance/FTUApps.com website coming soon.txt 94 Bytes20.9 - Reachability-Distance(A,B)/FTUApps.com website coming soon.txt 94 Bytes21.1 - Accuracy/FTUApps.com website coming soon.txt 94 Bytes21.10 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes21.2 - Confusion matrix, TPR, FPR, FNR, TNR/FTUApps.com website coming soon.txt 94 Bytes21.3 - Precision and recall, F1-score/FTUApps.com website coming soon.txt 94 Bytes21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC/FTUApps.com website coming soon.txt 94 Bytes21.5 - Log-loss/FTUApps.com website coming soon.txt 94 Bytes21.6 - R-SquaredCoefficient of determination/FTUApps.com website coming soon.txt 94 Bytes21.7 - Median absolute deviation (MAD)/FTUApps.com website coming soon.txt 94 Bytes21.8 - Distribution of errors/FTUApps.com website coming soon.txt 94 Bytes21.9 - Assignment-3 Apply k-Nearest Neighbor/FTUApps.com website coming soon.txt 94 Bytes22.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes23.1 - Conditional probability/FTUApps.com website coming soon.txt 94 Bytes23.10 - Bias and Variance tradeoff/FTUApps.com website coming soon.txt 94 Bytes23.11 - Feature importance and interpretability/FTUApps.com website coming soon.txt 94 Bytes23.12 - Imbalanced data/FTUApps.com website coming soon.txt 94 Bytes23.13 - Outliers/FTUApps.com website coming soon.txt 94 Bytes23.14 - Missing values/FTUApps.com website coming soon.txt 94 Bytes23.15 - Handling Numerical features (Gaussian NB)/FTUApps.com website coming soon.txt 94 Bytes23.16 - Multiclass classification/FTUApps.com website coming soon.txt 94 Bytes23.17 - Similarity or Distance matrix/FTUApps.com website coming soon.txt 94 Bytes23.18 - Large dimensionality/FTUApps.com website coming soon.txt 94 Bytes23.19 - Best and worst cases/FTUApps.com website coming soon.txt 94 Bytes23.2 - Independent vs Mutually exclusive events/FTUApps.com website coming soon.txt 94 Bytes23.20 - Code example/FTUApps.com website coming soon.txt 94 Bytes23.21 - Assignment-4 Apply Naive Bayes/FTUApps.com website coming soon.txt 94 Bytes23.22 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes23.3 - Bayes Theorem with examples/FTUApps.com website coming soon.txt 94 Bytes23.4 - Exercise problems on Bayes Theorem/FTUApps.com website coming soon.txt 94 Bytes23.5 - Naive Bayes algorithm/FTUApps.com website coming soon.txt 94 Bytes23.6 - Toy example Train and test stages/FTUApps.com website coming soon.txt 94 Bytes23.7 - Naive Bayes on Text data/FTUApps.com website coming soon.txt 94 Bytes23.8 - LaplaceAdditive Smoothing/FTUApps.com website coming soon.txt 94 Bytes23.9 - Log-probabilities for numerical stability/FTUApps.com website coming soon.txt 94 Bytes24.1 - Geometric intuition of Logistic Regression/FTUApps.com website coming soon.txt 94 Bytes24.10 - Column Standardization/FTUApps.com website coming soon.txt 94 Bytes24.11 - Feature importance and Model interpretability/FTUApps.com website coming soon.txt 94 Bytes24.12 - Collinearity of features/FTUApps.com website coming soon.txt 94 Bytes24.13 - TestRun time space and time complexity/FTUApps.com website coming soon.txt 94 Bytes24.14 - Real world cases/FTUApps.com website coming soon.txt 94 Bytes24.15 - Non-linearly separable data & feature engineering/FTUApps.com website coming soon.txt 94 Bytes24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/FTUApps.com website coming soon.txt 94 Bytes24.17 - Assignment-5 Apply Logistic Regression/FTUApps.com website coming soon.txt 94 Bytes24.18 - Extensions to Generalized linear models/FTUApps.com website coming soon.txt 94 Bytes24.2 - Sigmoid function Squashing/FTUApps.com website coming soon.txt 94 Bytes24.3 - Mathematical formulation of Objective function/FTUApps.com website coming soon.txt 94 Bytes24.4 - Weight vector/FTUApps.com website coming soon.txt 94 Bytes24.5 - L2 Regularization Overfitting and Underfitting/FTUApps.com website coming soon.txt 94 Bytes24.6 - L1 regularization and sparsity/FTUApps.com website coming soon.txt 94 Bytes24.7 - Probabilistic Interpretation Gaussian Naive Bayes/FTUApps.com website coming soon.txt 94 Bytes24.8 - Loss minimization interpretation/FTUApps.com website coming soon.txt 94 Bytes24.9 - hyperparameters and random search/FTUApps.com website coming soon.txt 94 Bytes25.1 - Geometric intuition of Linear Regression/FTUApps.com website coming soon.txt 94 Bytes25.2 - Mathematical formulation/FTUApps.com website coming soon.txt 94 Bytes25.3 - Real world Cases/FTUApps.com website coming soon.txt 94 Bytes25.4 - Code sample for Linear Regression/FTUApps.com website coming soon.txt 94 Bytes26.1 - Differentiation/FTUApps.com website coming soon.txt 94 Bytes26.10 - Logistic regression formulation revisited/FTUApps.com website coming soon.txt 94 Bytes26.11 - Why L1 regularization creates sparsity/FTUApps.com website coming soon.txt 94 Bytes26.12 - Assignment 6 Implement SGD for linear regression/FTUApps.com website coming soon.txt 94 Bytes26.13 - Revision questions/FTUApps.com website coming soon.txt 94 Bytes26.2 - Online differentiation tools/FTUApps.com website coming soon.txt 94 Bytes26.3 - Maxima and Minima/FTUApps.com website coming soon.txt 94 Bytes26.4 - Vector calculus Grad/FTUApps.com website coming soon.txt 94 Bytes26.5 - Gradient descent geometric intuition/FTUApps.com website coming soon.txt 94 Bytes26.6 - Learning rate/FTUApps.com website coming soon.txt 94 Bytes26.7 - Gradient descent for linear regression/FTUApps.com website coming soon.txt 94 Bytes26.8 - SGD algorithm/FTUApps.com website coming soon.txt 94 Bytes26.9 - Constrained Optimization & PCA/FTUApps.com website coming soon.txt 94 Bytes27.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes28.1 - Geometric Intution/FTUApps.com website coming soon.txt 94 Bytes28.10 - Train and run time complexities/FTUApps.com website coming soon.txt 94 Bytes28.11 - nu-SVM control errors and support vectors/FTUApps.com website coming soon.txt 94 Bytes28.12 - SVM Regression/FTUApps.com website coming soon.txt 94 Bytes28.13 - Cases/FTUApps.com website coming soon.txt 94 Bytes28.14 - Code Sample/FTUApps.com website coming soon.txt 94 Bytes28.15 - Assignment-7 Apply SVM/FTUApps.com website coming soon.txt 94 Bytes28.16 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes28.2 - Mathematical derivation/FTUApps.com website coming soon.txt 94 Bytes28.3 - Why we take values +1 and and -1 for Support vector planes/FTUApps.com website coming soon.txt 94 Bytes28.4 - Loss function (Hinge Loss) based interpretation/FTUApps.com website coming soon.txt 94 Bytes28.5 - Dual form of SVM formulation/FTUApps.com website coming soon.txt 94 Bytes28.6 - kernel trick/FTUApps.com website coming soon.txt 94 Bytes28.7 - Polynomial Kernel/FTUApps.com website coming soon.txt 94 Bytes28.8 - RBF-Kernel/FTUApps.com website coming soon.txt 94 Bytes28.9 - Domain specific Kernels/FTUApps.com website coming soon.txt 94 Bytes29.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes3.1 - Lists/FTUApps.com website coming soon.txt 94 Bytes3.2 - Tuples part 1/FTUApps.com website coming soon.txt 94 Bytes3.3 - Tuples part-2/FTUApps.com website coming soon.txt 94 Bytes3.4 - Sets/FTUApps.com website coming soon.txt 94 Bytes3.5 - Dictionary/FTUApps.com website coming soon.txt 94 Bytes3.6 - Strings/FTUApps.com website coming soon.txt 94 Bytes30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes/FTUApps.com website coming soon.txt 94 Bytes30.10 - Overfitting and Underfitting/FTUApps.com website coming soon.txt 94 Bytes30.11 - Train and Run time complexity/FTUApps.com website coming soon.txt 94 Bytes30.12 - Regression using Decision Trees/FTUApps.com website coming soon.txt 94 Bytes30.13 - Cases/FTUApps.com website coming soon.txt 94 Bytes30.14 - Code Samples/FTUApps.com website coming soon.txt 94 Bytes30.15 - Assignment-8 Apply Decision Trees/FTUApps.com website coming soon.txt 94 Bytes30.16 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes30.2 - Sample Decision tree/FTUApps.com website coming soon.txt 94 Bytes30.3 - Building a decision TreeEntropy/FTUApps.com website coming soon.txt 94 Bytes30.4 - Building a decision TreeInformation Gain/FTUApps.com website coming soon.txt 94 Bytes30.5 - Building a decision Tree Gini Impurity/FTUApps.com website coming soon.txt 94 Bytes30.6 - Building a decision Tree Constructing a DT/FTUApps.com website coming soon.txt 94 Bytes30.7 - Building a decision Tree Splitting numerical features/FTUApps.com website coming soon.txt 94 Bytes30.8 - Feature standardization/FTUApps.com website coming soon.txt 94 Bytes30.9 - Building a decision TreeCategorical features with many possible values/FTUApps.com website coming soon.txt 94 Bytes31.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes32.1 - What are ensembles/FTUApps.com website coming soon.txt 94 Bytes32.10 - Residuals, Loss functions and gradients/FTUApps.com website coming soon.txt 94 Bytes32.11 - Gradient Boosting/FTUApps.com website coming soon.txt 94 Bytes32.12 - Regularization by Shrinkage/FTUApps.com website coming soon.txt 94 Bytes32.13 - Train and Run time complexity/FTUApps.com website coming soon.txt 94 Bytes32.14 - XGBoost Boosting + Randomization/FTUApps.com website coming soon.txt 94 Bytes32.15 - AdaBoost geometric intuition/FTUApps.com website coming soon.txt 94 Bytes32.16 - Stacking models/FTUApps.com website coming soon.txt 94 Bytes32.17 - Cascading classifiers/FTUApps.com website coming soon.txt 94 Bytes32.18 - Kaggle competitions vs Real world/FTUApps.com website coming soon.txt 94 Bytes32.19 - Assignment-9 Apply Random Forests & GBDT/FTUApps.com website coming soon.txt 94 Bytes32.2 - Bootstrapped Aggregation (Bagging) Intuition/FTUApps.com website coming soon.txt 94 Bytes32.20 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes32.3 - Random Forest and their construction/FTUApps.com website coming soon.txt 94 Bytes32.4 - Bias-Variance tradeoff/FTUApps.com website coming soon.txt 94 Bytes32.5 - Train and run time complexity/FTUApps.com website coming soon.txt 94 Bytes32.6 - BaggingCode Sample/FTUApps.com website coming soon.txt 94 Bytes32.7 - Extremely randomized trees/FTUApps.com website coming soon.txt 94 Bytes32.8 - Random Tree Cases/FTUApps.com website coming soon.txt 94 Bytes32.9 - Boosting Intuition/FTUApps.com website coming soon.txt 94 Bytes33.1 - Introduction/FTUApps.com website coming soon.txt 94 Bytes33.10 - Indicator variables/FTUApps.com website coming soon.txt 94 Bytes33.11 - Feature binning/FTUApps.com website coming soon.txt 94 Bytes33.12 - Interaction variables/FTUApps.com website coming soon.txt 94 Bytes33.13 - Mathematical transforms/FTUApps.com website coming soon.txt 94 Bytes33.14 - Model specific featurizations/FTUApps.com website coming soon.txt 94 Bytes33.15 - Feature orthogonality/FTUApps.com website coming soon.txt 94 Bytes33.16 - Domain specific featurizations/FTUApps.com website coming soon.txt 94 Bytes33.17 - Feature slicing/FTUApps.com website coming soon.txt 94 Bytes33.18 - Kaggle Winners solutions/FTUApps.com website coming soon.txt 94 Bytes33.2 - Moving window for Time Series Data/FTUApps.com website coming soon.txt 94 Bytes33.3 - Fourier decomposition/FTUApps.com website coming soon.txt 94 Bytes33.4 - Deep learning features LSTM/FTUApps.com website coming soon.txt 94 Bytes33.5 - Image histogram/FTUApps.com website coming soon.txt 94 Bytes33.6 - Keypoints SIFT/FTUApps.com website coming soon.txt 94 Bytes33.7 - Deep learning features CNN/FTUApps.com website coming soon.txt 94 Bytes33.8 - Relational data/FTUApps.com website coming soon.txt 94 Bytes33.9 - Graph data/FTUApps.com website coming soon.txt 94 Bytes34.1 - Calibration of ModelsNeed for calibration/FTUApps.com website coming soon.txt 94 Bytes34.10 - AB testing/FTUApps.com website coming soon.txt 94 Bytes34.11 - Data Science Life cycle/FTUApps.com website coming soon.txt 94 Bytes34.12 - VC dimension/FTUApps.com website coming soon.txt 94 Bytes34.2 - Productionization and deployment of Machine Learning Models/FTUApps.com website coming soon.txt 94 Bytes34.3 - Calibration Plots/FTUApps.com website coming soon.txt 94 Bytes34.4 - Platt’s CalibrationScaling/FTUApps.com website coming soon.txt 94 Bytes34.5 - Isotonic Regression/FTUApps.com website coming soon.txt 94 Bytes34.6 - Code Samples/FTUApps.com website coming soon.txt 94 Bytes34.7 - Modeling in the presence of outliers RANSAC/FTUApps.com website coming soon.txt 94 Bytes34.8 - Productionizing models/FTUApps.com website coming soon.txt 94 Bytes34.9 - Retraining models periodically/FTUApps.com website coming soon.txt 94 Bytes35.1 - What is Clustering/FTUApps.com website coming soon.txt 94 Bytes35.10 - K-Medoids/FTUApps.com website coming soon.txt 94 Bytes35.11 - Determining the right K/FTUApps.com website coming soon.txt 94 Bytes35.12 - Code Samples/FTUApps.com website coming soon.txt 94 Bytes35.13 - Time and space complexity/FTUApps.com website coming soon.txt 94 Bytes35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/FTUApps.com website coming soon.txt 94 Bytes35.2 - Unsupervised learning/FTUApps.com website coming soon.txt 94 Bytes35.3 - Applications/FTUApps.com website coming soon.txt 94 Bytes35.4 - Metrics for Clustering/FTUApps.com website coming soon.txt 94 Bytes35.5 - K-Means Geometric intuition, Centroids/FTUApps.com website coming soon.txt 94 Bytes35.6 - K-Means Mathematical formulation Objective function/FTUApps.com website coming soon.txt 94 Bytes35.7 - K-Means Algorithm/FTUApps.com website coming soon.txt 94 Bytes35.8 - How to initialize K-Means++/FTUApps.com website coming soon.txt 94 Bytes35.9 - Failure casesLimitations/FTUApps.com website coming soon.txt 94 Bytes36.1 - Agglomerative & Divisive, Dendrograms/FTUApps.com website coming soon.txt 94 Bytes36.2 - Agglomerative Clustering/FTUApps.com website coming soon.txt 94 Bytes36.3 - Proximity methods Advantages and Limitations/FTUApps.com website coming soon.txt 94 Bytes36.4 - Time and Space Complexity/FTUApps.com website coming soon.txt 94 Bytes36.5 - Limitations of Hierarchical Clustering/FTUApps.com website coming soon.txt 94 Bytes36.6 - Code sample/FTUApps.com website coming soon.txt 94 Bytes36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/FTUApps.com website coming soon.txt 94 Bytes37.1 - Density based clustering/FTUApps.com website coming soon.txt 94 Bytes37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms/FTUApps.com website coming soon.txt 94 Bytes37.11 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes37.2 - MinPts and Eps Density/FTUApps.com website coming soon.txt 94 Bytes37.3 - Core, Border and Noise points/FTUApps.com website coming soon.txt 94 Bytes37.4 - Density edge and Density connected points/FTUApps.com website coming soon.txt 94 Bytes37.5 - DBSCAN Algorithm/FTUApps.com website coming soon.txt 94 Bytes37.6 - Hyper Parameters MinPts and Eps/FTUApps.com website coming soon.txt 94 Bytes37.7 - Advantages and Limitations of DBSCAN/FTUApps.com website coming soon.txt 94 Bytes37.8 - Time and Space Complexity/FTUApps.com website coming soon.txt 94 Bytes37.9 - Code samples/FTUApps.com website coming soon.txt 94 Bytes38.1 - Problem formulation Movie reviews/FTUApps.com website coming soon.txt 94 Bytes38.10 - Matrix Factorization for recommender systems Netflix Prize Solution/FTUApps.com website coming soon.txt 94 Bytes38.11 - Cold Start problem/FTUApps.com website coming soon.txt 94 Bytes38.12 - Word vectors as MF/FTUApps.com website coming soon.txt 94 Bytes38.13 - Eigen-Faces/FTUApps.com website coming soon.txt 94 Bytes38.14 - Code example/FTUApps.com website coming soon.txt 94 Bytes38.15 - Assignment-11 Apply Truncated SVD/FTUApps.com website coming soon.txt 94 Bytes38.16 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes38.2 - Content based vs Collaborative Filtering/FTUApps.com website coming soon.txt 94 Bytes38.3 - Similarity based Algorithms/FTUApps.com website coming soon.txt 94 Bytes38.4 - Matrix Factorization PCA, SVD/FTUApps.com website coming soon.txt 94 Bytes38.5 - Matrix Factorization NMF/FTUApps.com website coming soon.txt 94 Bytes38.6 - Matrix Factorization for Collaborative filtering/FTUApps.com website coming soon.txt 94 Bytes38.7 - Matrix Factorization for feature engineering/FTUApps.com website coming soon.txt 94 Bytes38.8 - Clustering as MF/FTUApps.com website coming soon.txt 94 Bytes38.9 - Hyperparameter tuning/FTUApps.com website coming soon.txt 94 Bytes39.1 - Questions & Answers/FTUApps.com website coming soon.txt 94 Bytes4.1 - Introduction/FTUApps.com website coming soon.txt 94 Bytes4.10 - Debugging Python/FTUApps.com website coming soon.txt 94 Bytes4.2 - Types of functions/FTUApps.com website coming soon.txt 94 Bytes4.3 - Function arguments/FTUApps.com website coming soon.txt 94 Bytes4.4 - Recursive functions/FTUApps.com website coming soon.txt 94 Bytes4.5 - Lambda functions/FTUApps.com website coming soon.txt 94 Bytes4.6 - Modules/FTUApps.com website coming soon.txt 94 Bytes4.7 - Packages/FTUApps.com website coming soon.txt 94 Bytes4.8 - File Handling/FTUApps.com website coming soon.txt 94 Bytes4.9 - Exception Handling/FTUApps.com website coming soon.txt 94 Bytes40.1 - BusinessReal world problem/FTUApps.com website coming soon.txt 94 Bytes40.10 - Data Modeling Multi label Classification/FTUApps.com website coming soon.txt 94 Bytes40.11 - Data preparation/FTUApps.com website coming soon.txt 94 Bytes40.12 - Train-Test Split/FTUApps.com website coming soon.txt 94 Bytes40.13 - Featurization/FTUApps.com website coming soon.txt 94 Bytes40.14 - Logistic regression One VS Rest/FTUApps.com website coming soon.txt 94 Bytes40.15 - Sampling data and tags+Weighted models/FTUApps.com website coming soon.txt 94 Bytes40.16 - Logistic regression revisited/FTUApps.com website coming soon.txt 94 Bytes40.17 - Why not use advanced techniques/FTUApps.com website coming soon.txt 94 Bytes40.18 - Assignments/FTUApps.com website coming soon.txt 94 Bytes40.2 - Business objectives and constraints/FTUApps.com website coming soon.txt 94 Bytes40.3 - Mapping to an ML problem Data overview/FTUApps.com website coming soon.txt 94 Bytes40.4 - Mapping to an ML problemML problem formulation/FTUApps.com website coming soon.txt 94 Bytes40.5 - Mapping to an ML problemPerformance metrics/FTUApps.com website coming soon.txt 94 Bytes40.6 - Hamming loss/FTUApps.com website coming soon.txt 94 Bytes40.7 - EDAData Loading/FTUApps.com website coming soon.txt 94 Bytes40.8 - EDAAnalysis of tags/FTUApps.com website coming soon.txt 94 Bytes40.9 - EDAData Preprocessing/FTUApps.com website coming soon.txt 94 Bytes41.1 - BusinessReal world problem Problem definition/FTUApps.com website coming soon.txt 94 Bytes41.10 - EDA Feature analysis/FTUApps.com website coming soon.txt 94 Bytes41.11 - EDA Data Visualization T-SNE/FTUApps.com website coming soon.txt 94 Bytes41.12 - EDA TF-IDF weighted Word2Vec featurization/FTUApps.com website coming soon.txt 94 Bytes41.13 - ML Models Loading Data/FTUApps.com website coming soon.txt 94 Bytes41.14 - ML Models Random Model/FTUApps.com website coming soon.txt 94 Bytes41.15 - ML Models Logistic Regression and Linear SVM/FTUApps.com website coming soon.txt 94 Bytes41.16 - ML Models XGBoost/FTUApps.com website coming soon.txt 94 Bytes41.17 - Assignments/FTUApps.com website coming soon.txt 94 Bytes41.2 - Business objectives and constraints/FTUApps.com website coming soon.txt 94 Bytes41.3 - Mapping to an ML problem Data overview/FTUApps.com website coming soon.txt 94 Bytes41.4 - Mapping to an ML problem ML problem and performance metric/FTUApps.com website coming soon.txt 94 Bytes41.5 - Mapping to an ML problem Train-test split/FTUApps.com website coming soon.txt 94 Bytes41.6 - EDA Basic Statistics/FTUApps.com website coming soon.txt 94 Bytes41.7 - EDA Basic Feature Extraction/FTUApps.com website coming soon.txt 94 Bytes41.8 - EDA Text Preprocessing/FTUApps.com website coming soon.txt 94 Bytes41.9 - EDA Advanced Feature Extraction/FTUApps.com website coming soon.txt 94 Bytes42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images/FTUApps.com website coming soon.txt 94 Bytes42.10 - Text Pre-Processing Tokenization and Stop-word removal/FTUApps.com website coming soon.txt 94 Bytes42.11 - Stemming/FTUApps.com website coming soon.txt 94 Bytes42.12 - Text based product similarity Converting text to an n-D vector bag of words/FTUApps.com website coming soon.txt 94 Bytes42.13 - Code for bag of words based product similarity/FTUApps.com website coming soon.txt 94 Bytes42.14 - TF-IDF featurizing text based on word-importance/FTUApps.com website coming soon.txt 94 Bytes42.15 - Code for TF-IDF based product similarity/FTUApps.com website coming soon.txt 94 Bytes42.16 - Code for IDF based product similarity/FTUApps.com website coming soon.txt 94 Bytes42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)/FTUApps.com website coming soon.txt 94 Bytes42.18 - Code for Average Word2Vec product similarity/FTUApps.com website coming soon.txt 94 Bytes42.19 - TF-IDF weighted Word2Vec/FTUApps.com website coming soon.txt 94 Bytes42.2 - Plan of action/FTUApps.com website coming soon.txt 94 Bytes42.20 - Code for IDF weighted Word2Vec product similarity/FTUApps.com website coming soon.txt 94 Bytes42.21 - Weighted similarity using brand and color/FTUApps.com website coming soon.txt 94 Bytes42.22 - Code for weighted similarity/FTUApps.com website coming soon.txt 94 Bytes42.23 - Building a real world solution/FTUApps.com website coming soon.txt 94 Bytes42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts/FTUApps.com website coming soon.txt 94 Bytes42.25 - Using Keras + Tensorflow to extract features/FTUApps.com website coming soon.txt 94 Bytes42.26 - Visual similarity based product similarity/FTUApps.com website coming soon.txt 94 Bytes42.27 - Measuring goodness of our solution AB testing/FTUApps.com website coming soon.txt 94 Bytes42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color/FTUApps.com website coming soon.txt 94 Bytes42.3 - Amazon product advertising API/FTUApps.com website coming soon.txt 94 Bytes42.4 - Data folders and paths/FTUApps.com website coming soon.txt 94 Bytes42.5 - Overview of the data and Terminology/FTUApps.com website coming soon.txt 94 Bytes42.6 - Data cleaning and understandingMissing data in various features/FTUApps.com website coming soon.txt 94 Bytes42.7 - Understand duplicate rows/FTUApps.com website coming soon.txt 94 Bytes42.8 - Remove duplicates Part 1/FTUApps.com website coming soon.txt 94 Bytes42.9 - Remove duplicates Part 2/FTUApps.com website coming soon.txt 94 Bytes43.1 - Businessreal world problem Problem definition/FTUApps.com website coming soon.txt 94 Bytes43.10 - ML models – using byte files only Random Model/FTUApps.com website coming soon.txt 94 Bytes43.11 - k-NN/FTUApps.com website coming soon.txt 94 Bytes43.12 - Logistic regression/FTUApps.com website coming soon.txt 94 Bytes43.13 - Random Forest and Xgboost/FTUApps.com website coming soon.txt 94 Bytes43.14 - ASM Files Feature extraction & Multiprocessing/FTUApps.com website coming soon.txt 94 Bytes43.15 - File-size feature/FTUApps.com website coming soon.txt 94 Bytes43.16 - Univariate analysis/FTUApps.com website coming soon.txt 94 Bytes43.17 - t-SNE analysis/FTUApps.com website coming soon.txt 94 Bytes43.18 - ML models on ASM file features/FTUApps.com website coming soon.txt 94 Bytes43.19 - Models on all features t-SNE/FTUApps.com website coming soon.txt 94 Bytes43.2 - Businessreal world problem Objectives and constraints/FTUApps.com website coming soon.txt 94 Bytes43.20 - Models on all features RandomForest and Xgboost/FTUApps.com website coming soon.txt 94 Bytes43.21 - Assignments/FTUApps.com website coming soon.txt 94 Bytes43.3 - Machine Learning problem mapping Data overview/FTUApps.com website coming soon.txt 94 Bytes43.4 - Machine Learning problem mapping ML problem/FTUApps.com website coming soon.txt 94 Bytes43.5 - Machine Learning problem mapping Train and test splitting/FTUApps.com website coming soon.txt 94 Bytes43.6 - Exploratory Data Analysis Class distribution/FTUApps.com website coming soon.txt 94 Bytes43.7 - Exploratory Data Analysis Feature extraction from byte files/FTUApps.com website coming soon.txt 94 Bytes43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files/FTUApps.com website coming soon.txt 94 Bytes43.9 - Exploratory Data Analysis Train-Test class distribution/FTUApps.com website coming soon.txt 94 Bytes44.1 - BusinessReal world problemProblem definition/FTUApps.com website coming soon.txt 94 Bytes44.10 - Exploratory Data AnalysisCold start problem/FTUApps.com website coming soon.txt 94 Bytes44.11 - Computing Similarity matricesUser-User similarity matrix/FTUApps.com website coming soon.txt 94 Bytes44.12 - Computing Similarity matricesMovie-Movie similarity/FTUApps.com website coming soon.txt 94 Bytes44.13 - Computing Similarity matricesDoes movie-movie similarity work/FTUApps.com website coming soon.txt 94 Bytes44.14 - ML ModelsSurprise library/FTUApps.com website coming soon.txt 94 Bytes44.15 - Overview of the modelling strategy/FTUApps.com website coming soon.txt 94 Bytes44.16 - Data Sampling/FTUApps.com website coming soon.txt 94 Bytes44.17 - Google drive with intermediate files/FTUApps.com website coming soon.txt 94 Bytes44.18 - Featurizations for regression/FTUApps.com website coming soon.txt 94 Bytes44.19 - Data transformation for Surprise/FTUApps.com website coming soon.txt 94 Bytes44.2 - Objectives and constraints/FTUApps.com website coming soon.txt 94 Bytes44.20 - Xgboost with 13 features/FTUApps.com website coming soon.txt 94 Bytes44.21 - Surprise Baseline model/FTUApps.com website coming soon.txt 94 Bytes44.22 - Xgboost + 13 features +Surprise baseline model/FTUApps.com website coming soon.txt 94 Bytes44.23 - Surprise KNN predictors/FTUApps.com website coming soon.txt 94 Bytes44.24 - Matrix Factorization models using Surprise/FTUApps.com website coming soon.txt 94 Bytes44.25 - SVD ++ with implicit feedback/FTUApps.com website coming soon.txt 94 Bytes44.26 - Final models with all features and predictors/FTUApps.com website coming soon.txt 94 Bytes44.27 - Comparison between various models/FTUApps.com website coming soon.txt 94 Bytes44.28 - Assignments/FTUApps.com website coming soon.txt 94 Bytes44.3 - Mapping to an ML problemData overview/FTUApps.com website coming soon.txt 94 Bytes44.4 - Mapping to an ML problemML problem formulation/FTUApps.com website coming soon.txt 94 Bytes44.5 - Exploratory Data AnalysisData preprocessing/FTUApps.com website coming soon.txt 94 Bytes44.6 - Exploratory Data AnalysisTemporal Train-Test split/FTUApps.com website coming soon.txt 94 Bytes44.7 - Exploratory Data AnalysisPreliminary data analysis/FTUApps.com website coming soon.txt 94 Bytes44.8 - Exploratory Data AnalysisSparse matrix representation/FTUApps.com website coming soon.txt 94 Bytes44.9 - Exploratory Data AnalysisAverage ratings for various slices/FTUApps.com website coming soon.txt 94 Bytes45.1 - BusinessReal world problem Overview/FTUApps.com website coming soon.txt 94 Bytes45.10 - Univariate AnalysisVariation Feature/FTUApps.com website coming soon.txt 94 Bytes45.11 - Univariate AnalysisText feature/FTUApps.com website coming soon.txt 94 Bytes45.12 - Machine Learning ModelsData preparation/FTUApps.com website coming soon.txt 94 Bytes45.13 - Baseline Model Naive Bayes/FTUApps.com website coming soon.txt 94 Bytes45.14 - K-Nearest Neighbors Classification/FTUApps.com website coming soon.txt 94 Bytes45.15 - Logistic Regression with class balancing/FTUApps.com website coming soon.txt 94 Bytes45.16 - Logistic Regression without class balancing/FTUApps.com website coming soon.txt 94 Bytes45.17 - Linear-SVM/FTUApps.com website coming soon.txt 94 Bytes45.18 - Random-Forest with one-hot encoded features/FTUApps.com website coming soon.txt 94 Bytes45.19 - Random-Forest with response-coded features/FTUApps.com website coming soon.txt 94 Bytes45.2 - Business objectives and constraints/FTUApps.com website coming soon.txt 94 Bytes45.20 - Stacking Classifier/FTUApps.com website coming soon.txt 94 Bytes45.21 - Majority Voting classifier/FTUApps.com website coming soon.txt 94 Bytes45.22 - Assignments/FTUApps.com website coming soon.txt 94 Bytes45.3 - ML problem formulation Data/FTUApps.com website coming soon.txt 94 Bytes45.4 - ML problem formulation Mapping real world to ML problem#/FTUApps.com website coming soon.txt 94 Bytes45.4 - ML problem formulation Mapping real world to ML problem/FTUApps.com website coming soon.txt 94 Bytes45.5 - ML problem formulation Train, CV and Test data construction/FTUApps.com website coming soon.txt 94 Bytes45.6 - Exploratory Data AnalysisReading data & preprocessing/FTUApps.com website coming soon.txt 94 Bytes45.7 - Exploratory Data AnalysisDistribution of Class-labels/FTUApps.com website coming soon.txt 94 Bytes45.8 - Exploratory Data Analysis “Random” Model/FTUApps.com website coming soon.txt 94 Bytes45.9 - Univariate AnalysisGene feature/FTUApps.com website coming soon.txt 94 Bytes46.1 - BusinessReal world problem Overview/FTUApps.com website coming soon.txt 94 Bytes46.10 - Data Cleaning Speed/FTUApps.com website coming soon.txt 94 Bytes46.11 - Data Cleaning Distance/FTUApps.com website coming soon.txt 94 Bytes46.12 - Data Cleaning Fare/FTUApps.com website coming soon.txt 94 Bytes46.13 - Data Cleaning Remove all outlierserroneous points/FTUApps.com website coming soon.txt 94 Bytes46.14 - Data PreparationClusteringSegmentation/FTUApps.com website coming soon.txt 94 Bytes46.15 - Data PreparationTime binning/FTUApps.com website coming soon.txt 94 Bytes46.16 - Data PreparationSmoothing time-series data/FTUApps.com website coming soon.txt 94 Bytes46.17 - Data PreparationSmoothing time-series data cont/FTUApps.com website coming soon.txt 94 Bytes46.18 - Data Preparation Time series and Fourier transforms/FTUApps.com website coming soon.txt 94 Bytes46.19 - Ratios and previous-time-bin values/FTUApps.com website coming soon.txt 94 Bytes46.2 - Objectives and Constraints/FTUApps.com website coming soon.txt 94 Bytes46.20 - Simple moving average/FTUApps.com website coming soon.txt 94 Bytes46.21 - Weighted Moving average/FTUApps.com website coming soon.txt 94 Bytes46.22 - Exponential weighted moving average/FTUApps.com website coming soon.txt 94 Bytes46.23 - Results/FTUApps.com website coming soon.txt 94 Bytes46.24 - Regression models Train-Test split & Features/FTUApps.com website coming soon.txt 94 Bytes46.25 - Linear regression/FTUApps.com website coming soon.txt 94 Bytes46.26 - Random Forest regression/FTUApps.com website coming soon.txt 94 Bytes46.27 - Xgboost Regression/FTUApps.com website coming soon.txt 94 Bytes46.28 - Model comparison/FTUApps.com website coming soon.txt 94 Bytes46.29 - Assignment/FTUApps.com website coming soon.txt 94 Bytes46.3 - Mapping to ML problem Data/FTUApps.com website coming soon.txt 94 Bytes46.4 - Mapping to ML problem dask dataframes/FTUApps.com website coming soon.txt 94 Bytes46.5 - Mapping to ML problem FieldsFeatures/FTUApps.com website coming soon.txt 94 Bytes46.6 - Mapping to ML problem Time series forecastingRegression/FTUApps.com website coming soon.txt 94 Bytes46.7 - Mapping to ML problem Performance metrics/FTUApps.com website coming soon.txt 94 Bytes46.8 - Data Cleaning Latitude and Longitude data/FTUApps.com website coming soon.txt 94 Bytes46.9 - Data Cleaning Trip Duration/FTUApps.com website coming soon.txt 94 Bytes47.1 - History of Neural networks and Deep Learning/FTUApps.com website coming soon.txt 94 Bytes47.10 - Backpropagation/FTUApps.com website coming soon.txt 94 Bytes47.11 - Activation functions/FTUApps.com website coming soon.txt 94 Bytes47.12 - Vanishing Gradient problem/FTUApps.com website coming soon.txt 94 Bytes47.13 - Bias-Variance tradeoff/FTUApps.com website coming soon.txt 94 Bytes47.14 - Decision surfaces Playground/FTUApps.com website coming soon.txt 94 Bytes47.2 - How Biological Neurons work/FTUApps.com website coming soon.txt 94 Bytes47.3 - Growth of biological neural networks/FTUApps.com website coming soon.txt 94 Bytes47.4 - Diagrammatic representation Logistic Regression and Perceptron/FTUApps.com website coming soon.txt 94 Bytes47.5 - Multi-Layered Perceptron (MLP)/FTUApps.com website coming soon.txt 94 Bytes47.6 - Notation/FTUApps.com website coming soon.txt 94 Bytes47.7 - Training a single-neuron model/FTUApps.com website coming soon.txt 94 Bytes47.8 - Training an MLP Chain Rule/FTUApps.com website coming soon.txt 94 Bytes47.9 - Training an MLPMemoization/FTUApps.com website coming soon.txt 94 Bytes48.1 - Deep Multi-layer perceptrons1980s to 2010s/FTUApps.com website coming soon.txt 94 Bytes48.10 - Nesterov Accelerated Gradient (NAG)/FTUApps.com website coming soon.txt 94 Bytes48.11 - OptimizersAdaGrad/FTUApps.com website coming soon.txt 94 Bytes48.12 - Optimizers Adadelta andRMSProp/FTUApps.com website coming soon.txt 94 Bytes48.13 - Adam/FTUApps.com website coming soon.txt 94 Bytes48.14 - Which algorithm to choose when/FTUApps.com website coming soon.txt 94 Bytes48.15 - Gradient Checking and clipping/FTUApps.com website coming soon.txt 94 Bytes48.16 - Softmax and Cross-entropy for multi-class classification/FTUApps.com website coming soon.txt 94 Bytes48.17 - How to train a Deep MLP/FTUApps.com website coming soon.txt 94 Bytes48.18 - Auto Encoders/FTUApps.com website coming soon.txt 94 Bytes48.19 - Word2Vec CBOW/FTUApps.com website coming soon.txt 94 Bytes48.2 - Dropout layers & Regularization/FTUApps.com website coming soon.txt 94 Bytes48.20 - Word2Vec Skip-gram/FTUApps.com website coming soon.txt 94 Bytes48.21 - Word2Vec Algorithmic Optimizations/FTUApps.com website coming soon.txt 94 Bytes48.3 - Rectified Linear Units (ReLU)/FTUApps.com website coming soon.txt 94 Bytes48.4 - Weight initialization/FTUApps.com website coming soon.txt 94 Bytes48.5 - Batch Normalization/FTUApps.com website coming soon.txt 94 Bytes48.6 - OptimizersHill-descent analogy in 2D/FTUApps.com website coming soon.txt 94 Bytes48.7 - OptimizersHill descent in 3D and contours/FTUApps.com website coming soon.txt 94 Bytes48.8 - SGD Recap/FTUApps.com website coming soon.txt 94 Bytes48.9 - Batch SGD with momentum/FTUApps.com website coming soon.txt 94 Bytes49.1 - Tensorflow and Keras overview/FTUApps.com website coming soon.txt 94 Bytes49.10 - Model 3 Batch Normalization/FTUApps.com website coming soon.txt 94 Bytes49.11 - Model 4 Dropout/FTUApps.com website coming soon.txt 94 Bytes49.12 - MNIST classification in Keras/FTUApps.com website coming soon.txt 94 Bytes49.13 - Hyperparameter tuning in Keras/FTUApps.com website coming soon.txt 94 Bytes49.14 - Exercise Try different MLP architectures on MNIST dataset/FTUApps.com website coming soon.txt 94 Bytes49.2 - GPU vs CPU for Deep Learning/FTUApps.com website coming soon.txt 94 Bytes49.3 - Google Colaboratory/FTUApps.com website coming soon.txt 94 Bytes49.4 - Install TensorFlow/FTUApps.com website coming soon.txt 94 Bytes49.5 - Online documentation and tutorials/FTUApps.com website coming soon.txt 94 Bytes49.6 - Softmax Classifier on MNIST dataset/FTUApps.com website coming soon.txt 94 Bytes49.7 - MLP Initialization/FTUApps.com website coming soon.txt 94 Bytes49.8 - Model 1 Sigmoid activation/FTUApps.com website coming soon.txt 94 Bytes49.9 - Model 2 ReLU activation/FTUApps.com website coming soon.txt 94 Bytes5.1 - Numpy Introduction/FTUApps.com website coming soon.txt 94 Bytes5.2 - Numerical operations on Numpy/FTUApps.com website coming soon.txt 94 Bytes50.1 - Biological inspiration Visual Cortex/FTUApps.com website coming soon.txt 94 Bytes50.10 - Data Augmentation/FTUApps.com website coming soon.txt 94 Bytes50.11 - Convolution Layers in Keras/FTUApps.com website coming soon.txt 94 Bytes50.12 - AlexNet/FTUApps.com website coming soon.txt 94 Bytes50.13 - VGGNet/FTUApps.com website coming soon.txt 94 Bytes50.14 - Residual Network/FTUApps.com website coming soon.txt 94 Bytes50.15 - Inception Network/FTUApps.com website coming soon.txt 94 Bytes50.16 - What is Transfer learning/FTUApps.com website coming soon.txt 94 Bytes50.17 - Code example Cats vs Dogs/FTUApps.com website coming soon.txt 94 Bytes50.18 - Code Example MNIST dataset/FTUApps.com website coming soon.txt 94 Bytes50.19 - Assignment Try various CNN networks on MNIST dataset#/FTUApps.com website coming soon.txt 94 Bytes50.2 - ConvolutionEdge Detection on images/FTUApps.com website coming soon.txt 94 Bytes50.3 - ConvolutionPadding and strides/FTUApps.com website coming soon.txt 94 Bytes50.4 - Convolution over RGB images/FTUApps.com website coming soon.txt 94 Bytes50.5 - Convolutional layer/FTUApps.com website coming soon.txt 94 Bytes50.6 - Max-pooling/FTUApps.com website coming soon.txt 94 Bytes50.7 - CNN Training Optimization/FTUApps.com website coming soon.txt 94 Bytes50.8 - Example CNN LeNet [1998]/FTUApps.com website coming soon.txt 94 Bytes50.9 - ImageNet dataset/FTUApps.com website coming soon.txt 94 Bytes51.1 - Why RNNs/FTUApps.com website coming soon.txt 94 Bytes51.10 - Code example IMDB Sentiment classification/FTUApps.com website coming soon.txt 94 Bytes51.11 - Exercise Amazon Fine Food reviews LSTM model/FTUApps.com website coming soon.txt 94 Bytes51.2 - Recurrent Neural Network/FTUApps.com website coming soon.txt 94 Bytes51.3 - Training RNNs Backprop/FTUApps.com website coming soon.txt 94 Bytes51.4 - Types of RNNs/FTUApps.com website coming soon.txt 94 Bytes51.5 - Need for LSTMGRU/FTUApps.com website coming soon.txt 94 Bytes51.6 - LSTM/FTUApps.com website coming soon.txt 94 Bytes51.7 - GRUs/FTUApps.com website coming soon.txt 94 Bytes51.8 - Deep RNN/FTUApps.com website coming soon.txt 94 Bytes51.9 - Bidirectional RNN/FTUApps.com website coming soon.txt 94 Bytes52.1 - Questions and Answers/FTUApps.com website coming soon.txt 94 Bytes53.1 - Self Driving Car Problem definition/FTUApps.com website coming soon.txt 94 Bytes53.10 - NVIDIA’s end to end CNN model/FTUApps.com website coming soon.txt 94 Bytes53.11 - Train the model/FTUApps.com website coming soon.txt 94 Bytes53.12 - Test and visualize the output/FTUApps.com website coming soon.txt 94 Bytes53.13 - Extensions/FTUApps.com website coming soon.txt 94 Bytes53.14 - Assignment/FTUApps.com website coming soon.txt 94 Bytes53.2 - Datasets#/FTUApps.com website coming soon.txt 94 Bytes53.2 - Datasets/FTUApps.com website coming soon.txt 94 Bytes53.3 - Data understanding & Analysis Files and folders/FTUApps.com website coming soon.txt 94 Bytes53.4 - Dash-cam images and steering angles/FTUApps.com website coming soon.txt 94 Bytes53.5 - Split the dataset Train vs Test/FTUApps.com website coming soon.txt 94 Bytes53.6 - EDA Steering angles/FTUApps.com website coming soon.txt 94 Bytes53.7 - Mean Baseline model simple/FTUApps.com website coming soon.txt 94 Bytes53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/FTUApps.com website coming soon.txt 94 Bytes53.9 - Batch load the dataset/FTUApps.com website coming soon.txt 94 Bytes54.1 - Real-world problem/FTUApps.com website coming soon.txt 94 Bytes54.10 - MIDI music generation/FTUApps.com website coming soon.txt 94 Bytes54.11 - Survey blog/FTUApps.com website coming soon.txt 94 Bytes54.2 - Music representation/FTUApps.com website coming soon.txt 94 Bytes54.3 - Char-RNN with abc-notation Char-RNN model/FTUApps.com website coming soon.txt 94 Bytes54.4 - Char-RNN with abc-notation Data preparation/FTUApps.com website coming soon.txt 94 Bytes54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer/FTUApps.com website coming soon.txt 94 Bytes54.6 - Char-RNN with abc-notation State full RNN/FTUApps.com website coming soon.txt 94 Bytes54.7 - Char-RNN with abc-notation Model architecture,Model training/FTUApps.com website coming soon.txt 94 Bytes54.8 - Char-RNN with abc-notation Music generation/FTUApps.com website coming soon.txt 94 Bytes54.9 - Char-RNN with abc-notation Generate tabla music/FTUApps.com website coming soon.txt 94 Bytes55.1 - Human Activity Recognition Problem definition/FTUApps.com website coming soon.txt 94 Bytes55.2 - Dataset understanding/FTUApps.com website coming soon.txt 94 Bytes55.3 - Data cleaning & preprocessing/FTUApps.com website coming soon.txt 94 Bytes55.4 - EDAUnivariate analysis/FTUApps.com website coming soon.txt 94 Bytes55.5 - EDAData visualization using t-SNE/FTUApps.com website coming soon.txt 94 Bytes55.6 - Classical ML models/FTUApps.com website coming soon.txt 94 Bytes55.7 - Deep-learning Model/FTUApps.com website coming soon.txt 94 Bytes55.8 - Exercise Build deeper LSTM models and hyper-param tune them/FTUApps.com website coming soon.txt 94 Bytes56.1 - Problem definition/FTUApps.com website coming soon.txt 94 Bytes56.10 - Feature engineering on GraphsJaccard & Cosine Similarities/FTUApps.com website coming soon.txt 94 Bytes56.11 - PageRank/FTUApps.com website coming soon.txt 94 Bytes56.12 - Shortest Path/FTUApps.com website coming soon.txt 94 Bytes56.13 - Connected-components/FTUApps.com website coming soon.txt 94 Bytes56.14 - Adar Index/FTUApps.com website coming soon.txt 94 Bytes56.15 - Kartz Centrality/FTUApps.com website coming soon.txt 94 Bytes56.16 - HITS Score/FTUApps.com website coming soon.txt 94 Bytes56.17 - SVD/FTUApps.com website coming soon.txt 94 Bytes56.18 - Weight features/FTUApps.com website coming soon.txt 94 Bytes56.19 - Modeling/FTUApps.com website coming soon.txt 94 Bytes56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path/FTUApps.com website coming soon.txt 94 Bytes56.3 - Data format & Limitations/FTUApps.com website coming soon.txt 94 Bytes56.4 - Mapping to a supervised classification problem/FTUApps.com website coming soon.txt 94 Bytes56.5 - Business constraints & Metrics/FTUApps.com website coming soon.txt 94 Bytes56.6 - EDABasic Stats/FTUApps.com website coming soon.txt 94 Bytes56.7 - EDAFollower and following stats/FTUApps.com website coming soon.txt 94 Bytes56.8 - EDABinary Classification Task/FTUApps.com website coming soon.txt 94 Bytes56.9 - EDATrain and test split/FTUApps.com website coming soon.txt 94 Bytes57.1 - Introduction to Databases/FTUApps.com website coming soon.txt 94 Bytes57.10 - ORDER BY/FTUApps.com website coming soon.txt 94 Bytes57.11 - DISTINCT/FTUApps.com website coming soon.txt 94 Bytes57.12 - WHERE, Comparison operators, NULL/FTUApps.com website coming soon.txt 94 Bytes57.13 - Logical Operators/FTUApps.com website coming soon.txt 94 Bytes57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/FTUApps.com website coming soon.txt 94 Bytes57.15 - GROUP BY/FTUApps.com website coming soon.txt 94 Bytes57.16 - HAVING/FTUApps.com website coming soon.txt 94 Bytes57.17 - Order of keywords#/FTUApps.com website coming soon.txt 94 Bytes57.18 - Join and Natural Join/FTUApps.com website coming soon.txt 94 Bytes57.19 - Inner, Left, Right and Outer joins/FTUApps.com website coming soon.txt 94 Bytes57.2 - Why SQL/FTUApps.com website coming soon.txt 94 Bytes57.20 - Sub QueriesNested QueriesInner Queries/FTUApps.com website coming soon.txt 94 Bytes57.21 - DMLINSERT/FTUApps.com website coming soon.txt 94 Bytes57.22 - DMLUPDATE , DELETE/FTUApps.com website coming soon.txt 94 Bytes57.23 - DDLCREATE TABLE/FTUApps.com website coming soon.txt 94 Bytes57.24 - DDLALTER ADD, MODIFY, DROP/FTUApps.com website coming soon.txt 94 Bytes57.25 - DDLDROP TABLE, TRUNCATE, DELETE/FTUApps.com website coming soon.txt 94 Bytes57.26 - Data Control Language GRANT, REVOKE/FTUApps.com website coming soon.txt 94 Bytes57.27 - Learning resources/FTUApps.com website coming soon.txt 94 Bytes57.3 - Execution of an SQL statement/FTUApps.com website coming soon.txt 94 Bytes57.4 - IMDB dataset/FTUApps.com website coming soon.txt 94 Bytes57.5 - Installing MySQL/FTUApps.com website coming soon.txt 94 Bytes57.6 - Load IMDB data/FTUApps.com website coming soon.txt 94 Bytes57.7 - USE, DESCRIBE, SHOW TABLES/FTUApps.com website coming soon.txt 94 Bytes57.8 - SELECT/FTUApps.com website coming soon.txt 94 Bytes57.9 - LIMIT, OFFSET/FTUApps.com website coming soon.txt 94 Bytes58.1 - AD-Click Predicition/FTUApps.com website coming soon.txt 94 Bytes59.1 - Revision Questions/FTUApps.com website coming soon.txt 94 Bytes59.2 - Questions/FTUApps.com website coming soon.txt 94 Bytes59.3 - External resources for Interview Questions/FTUApps.com website coming soon.txt 94 Bytes6.1 - Getting started with Matplotlib/FTUApps.com website coming soon.txt 94 Bytes7.1 - Getting started with pandas/FTUApps.com website coming soon.txt 94 Bytes7.2 - Data Frame Basics/FTUApps.com website coming soon.txt 94 Bytes7.3 - Key Operations on Data Frames/FTUApps.com website coming soon.txt 94 Bytes8.1 - Space and Time Complexity Find largest number in a list/FTUApps.com website coming soon.txt 94 Bytes8.2 - Binary search/FTUApps.com website coming soon.txt 94 Bytes8.3 - Find elements common in two lists/FTUApps.com website coming soon.txt 94 Bytes8.4 - Find elements common in two lists using a HashtableDict/FTUApps.com website coming soon.txt 94 Bytes9.1 - Introduction to IRIS dataset and 2D scatter plot/FTUApps.com website coming soon.txt 94 Bytes9.10 - Percentiles and Quantiles/FTUApps.com website coming soon.txt 94 Bytes9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/FTUApps.com website coming soon.txt 94 Bytes9.12 - Box-plot with Whiskers/FTUApps.com website coming soon.txt 94 Bytes9.13 - Violin Plots/FTUApps.com website coming soon.txt 94 Bytes9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/FTUApps.com website coming soon.txt 94 Bytes9.15 - Multivariate Probability Density, Contour Plot/FTUApps.com website coming soon.txt 94 Bytes9.16 - Exercise Perform EDA on Haberman dataset/FTUApps.com website coming soon.txt 94 Bytes9.2 - 3D scatter plot/FTUApps.com website coming soon.txt 94 Bytes9.3 - Pair plots/FTUApps.com website coming soon.txt 94 Bytes9.4 - Limitations of Pair Plots/FTUApps.com website coming soon.txt 94 Bytes9.5 - Histogram and Introduction to PDF(Probability Density Function)/FTUApps.com website coming soon.txt 94 Bytes9.6 - Univariate Analysis using PDF/FTUApps.com website coming soon.txt 94 Bytes9.7 - CDF(Cumulative Distribution Function)/FTUApps.com website coming soon.txt 94 Bytes9.8 - Mean, Variance and Standard Deviation/FTUApps.com website coming soon.txt 94 Bytes9.9 - Median/FTUApps.com website coming soon.txt 94 Bytes
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