magnet:?xt=urn:btih:8571D7502767DA669CA0DFCD565B69D0DC35D06E
37 - Convolutional Neural Networks/010 dataset.zip 232.4 MB37 - Convolutional Neural Networks/001 dataset.zip 232.0 MB
37 - Convolutional Neural Networks/016 Hands-on CNN Training Using Jupyter Notebook for Image Classification.mp4 76.3 MB41 - Kernel PCA/002 Implementing Kernel PCA for Non-Linear Data Step-by-Step Guide.mp4 72.2 MB43 - Model Selection/004 Optimizing SVM Models with GridSearchCV A Step-by-Step Python Tutorial.mp4 71.1 MB40 - Linear Discriminant Analysis (LDA)/003 Step-by-Step Guide Applying LDA for Feature Extraction in Machine Learning.mp4 68.1 MB43 - Model Selection/005 Evaluating ML Model Accuracy K-Fold Cross-Validation Implementation in R.mp4 65.9 MB20 - Naive Bayes/001 Understanding Bayes--' Theorem Intuitively From Probability to Machine Learning.mp4 65.6 MB29 - Apriori/008 Step 3 Optimizing Product Placement - Apriori Algorithm, Lift --& Confidence.mp4 65.2 MB29 - Apriori/006 Step 1 - Creating a Sparse Matrix for Association Rule Mining in R.mp4 64.2 MB29 - Apriori/005 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python.mp4 63.6 MB37 - Convolutional Neural Networks/007 Step 4 - Fully Connected Layers in CNNs Optimizing Feature Combination.mp4 62.6 MB33 - Thompson Sampling/008 Step 1 - Thompson Sampling vs UCB Optimizing Ad Click-Through Rates in R.mp4 61.5 MB33 - Thompson Sampling/001 Understanding Thompson Sampling Algorithm Intuition and Implementation.mp4 61.3 MB44 - XGBoost/003 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems.mp4 60.3 MB36 - Artificial Neural Networks/011 Step 2 - TensorFlow 2.0 Tutorial Preprocessing Data for Customer Churn Model.mp4 60.1 MB37 - Convolutional Neural Networks/009 Deep Learning Essentials Understanding Softmax and Cross-Entropy in CNNs.mp4 59.2 MB29 - Apriori/001 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules.mp4 58.8 MB32 - Upper Confidence Bound (UCB)/012 Step 3 Optimizing Ad Selection - UCB --& Multi-Armed Bandit Algorithm Explained.mp4 58.4 MB37 - Convolutional Neural Networks/013 Step 3 - TensorFlow CNN Convolution to Output Layer for Vision Tasks.mp4 57.9 MB07 - Multiple Linear Regression/023 Optimizing Multiple Regression Models Backward Elimination Technique in R.mp4 57.6 MB34 - -------------------- Part 7 Natural Language Processing --------------------/024 Step 10 - Building a Document-Term Matrix for NLP Text Classification.mp4 57.5 MB37 - Convolutional Neural Networks/012 Step 2 - Keras ImageDataGenerator Prevent Overfitting in CNN Models.mp4 57.4 MB34 - -------------------- Part 7 Natural Language Processing --------------------/010 Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis.mp4 56.2 MB36 - Artificial Neural Networks/015 Step 1 - How to Preprocess Data for Artificial Neural Networks in R.mp4 55.9 MB45 - Annex Logistic Regression (Long Explanation)/001 Logistic Regression Intuition.mp4 55.2 MB29 - Apriori/003 Step 2 - Creating a List of Transactions for Market Basket Analysis in Python.mp4 55.2 MB34 - -------------------- Part 7 Natural Language Processing --------------------/005 Implementing Bag of Words in NLP A Step-by-Step Tutorial.mp4 55.2 MB39 - Principal Component Analysis (PCA)/002 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit-learn.mp4 54.4 MB34 - -------------------- Part 7 Natural Language Processing --------------------/014 Step 1 - Text Classification Using Bag-of-Words and Random Forest in R.mp4 53.6 MB37 - Convolutional Neural Networks/003 Step 1 - Understanding Convolution in CNNs Feature Detection and Feature Maps.mp4 53.0 MB36 - Artificial Neural Networks/014 Step 5 - Implementing ANN for Churn Prediction From Model to Confusion Matrix.mp4 53.0 MB36 - Artificial Neural Networks/002 Deep Learning Basics Exploring Neurons, Synapses, and Activation Functions.mp4 52.2 MB32 - Upper Confidence Bound (UCB)/011 Step 2 - UCB Algorithm in R Calculating Average Reward --& Confidence Interval.mp4 51.7 MB37 - Convolutional Neural Networks/002 Introduction to CNNs Understanding Deep Learning for Computer Vision.mp4 51.1 MB32 - Upper Confidence Bound (UCB)/006 Step 4 - Python for RL Coding the UCB Algorithm Step-by-Step.mp4 50.9 MB32 - Upper Confidence Bound (UCB)/001 Multi-Armed Bandit Exploration vs Exploitation in Reinforcement Learning.mp4 50.7 MB07 - Multiple Linear Regression/007 Backward Elimination Building Robust Multiple Linear Regression Models.mp4 50.6 MB37 - Convolutional Neural Networks/015 Step 5 - Making Single Predictions with Convolutional Neural Networks in Python.mp4 48.2 MB40 - Linear Discriminant Analysis (LDA)/002 Mastering Linear Discriminant Analysis Step-by-Step Python Implementation.mp4 48.0 MB44 - XGBoost/001 How to Use XGBoost in Python for Cancer Prediction with High Accuracy.mp4 47.8 MB37 - Convolutional Neural Networks/005 Step 2 - Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition.mp4 47.6 MB36 - Artificial Neural Networks/018 Step 4 - H2O Deep Learning Making Predictions and Evaluating Model Accuracy.mp4 47.6 MB43 - Model Selection/006 Optimizing SVM Models with Grid Search A Step-by-Step R Tutorial.mp4 47.4 MB29 - Apriori/007 Step 2 - Optimizing Apriori Model Choosing Minimum Support and Confidence.mp4 47.3 MB43 - Model Selection/003 K-Fold Cross-Validation in Python Improve Machine Learning Model Performance.mp4 46.9 MB36 - Artificial Neural Networks/012 Step 3 - Designing ANN Sequential Model --& Dense Layers for Deep Learning.mp4 46.7 MB32 - Upper Confidence Bound (UCB)/002 Upper Confidence Bound Algorithm Solving Multi-Armed Bandit Problems in ML.mp4 45.9 MB39 - Principal Component Analysis (PCA)/006 Step 3 - Implementing PCA and SVM for Customer Segmentation Practical Guide.mp4 45.8 MB33 - Thompson Sampling/005 Step 3 - Python Code for Thompson Sampling Maximizing Random Beta Distributions.mp4 45.4 MB35 - -------------------- Part 8 Deep Learning --------------------/002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 45.0 MB20 - Naive Bayes/002 Understanding Naive Bayes Algorithm Probabilistic Classification Explained.mp4 44.4 MB32 - Upper Confidence Bound (UCB)/010 Step 1 - Exploring Upper Confidence Bound in R Multi-Armed Bandit Problems.mp4 44.1 MB34 - -------------------- Part 7 Natural Language Processing --------------------/023 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning.mp4 42.3 MB36 - Artificial Neural Networks/005 How Do Neural Networks Learn Deep Learning Fundamentals Explained.mp4 41.9 MB36 - Artificial Neural Networks/017 Step 3 Building Deep Learning Model - H2O Neural Network Layer Config.mp4 41.8 MB34 - -------------------- Part 7 Natural Language Processing --------------------/008 Step 3 - Text Cleaning for NLP Remove Punctuation and Convert to Lowercase.mp4 41.7 MB29 - Apriori/004 Step 3 - Configuring Apriori Function Support, Confidence, and Lift in Python.mp4 41.4 MB36 - Artificial Neural Networks/004 How Do Neural Networks Work Step-by-Step Guide to Deep Learning Algorithms.mp4 41.3 MB32 - Upper Confidence Bound (UCB)/003 Step 1 - Upper Confidence Bound Solving Multi-Armed Bandit Problem in Python.mp4 41.0 MB33 - Thompson Sampling/004 Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python.mp4 39.9 MB19 - Kernel SVM/003 Kernel Trick SVM Machine Learning for Non-Linear Classification.mp4 39.8 MB39 - Principal Component Analysis (PCA)/004 Step 1 in R - Understanding Principal Component Analysis for Feature Extraction.mp4 39.3 MB30 - Eclat/002 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining.mp4 38.7 MB36 - Artificial Neural Networks/013 Step 4 - Train Neural Network Compile --& Fit for Customer Churn Prediction.mp4 38.6 MB07 - Multiple Linear Regression/006 Understanding P-Values and Statistical Significance in Hypothesis Testing.mp4 37.9 MB37 - Convolutional Neural Networks/011 Step 1 Intro to CNNs for Image Classification.mp4 37.4 MB39 - Principal Component Analysis (PCA)/005 Step 2 - Using preProcess Function in R for PCA Extracting Principal Components.mp4 36.7 MB34 - -------------------- Part 7 Natural Language Processing --------------------/004 From IfElse Rules to CNNs Evolution of Natural Language Processing.mp4 36.7 MB27 - Hierarchical Clustering/003 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting.mp4 36.7 MB19 - Kernel SVM/005 Mastering Support Vector Regression Non-Linear SVR with RBF Kernel Explained.mp4 35.8 MB10 - Decision Tree Regression/001 How to Build a Regression Tree Step-by-Step Guide for Machine Learning.mp4 35.7 MB41 - Kernel PCA/001 Kernel PCA in Python Improving Classification Accuracy with Feature Extraction.mp4 35.7 MB34 - -------------------- Part 7 Natural Language Processing --------------------/009 Step 4 - Text Preprocessing Stemming and Stop Word Removal for NLP in Python.mp4 35.5 MB24 - Evaluating Classification Models Performance/003 Understanding CAP Curves Assessing Model Performance in Data Science 2024.mp4 34.1 MB36 - Artificial Neural Networks/010 Step 1 ANN in Python Predicting Customer Churn with TensorFlow.mp4 33.4 MB30 - Eclat/003 Eclat vs Apriori Simplified Association Rule Learning in Data Mining.mp4 33.3 MB18 - Support Vector Machine (SVM)/001 Support Vector Machines Explained Hyperplanes and Support Vectors in ML.mp4 33.2 MB36 - Artificial Neural Networks/006 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization.mp4 33.0 MB29 - Apriori/002 Step 1 - Association Rule Learning Boost Sales with Python Data Mining.mp4 32.1 MB34 - -------------------- Part 7 Natural Language Processing --------------------/011 Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis.mp4 31.9 MB43 - Model Selection/001 Mastering Model Evaluation K-Fold Cross-Validation Techniques Explained.mp4 31.1 MB20 - Naive Bayes/004 Why is Naive Bayes Called Naive Understanding the Algorithm--'s Assumptions.mp4 30.8 MB14 - Regression Model Selection in R/002 Linear Regression Analysis Interpreting Coefficients for Business Decisions.mp4 30.4 MB14 - Regression Model Selection in R/001 Optimizing Regression Models R-Squared vs Adjusted R-Squared Explained.mp4 28.8 MB36 - Artificial Neural Networks/007 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals.mp4 28.7 MB27 - Hierarchical Clustering/002 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering.mp4 28.4 MB34 - -------------------- Part 7 Natural Language Processing --------------------/016 Step 2 - NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis.mp4 28.0 MB36 - Artificial Neural Networks/003 Neural Network Basics Understanding Activation Functions in Deep Learning.mp4 27.4 MB33 - Thompson Sampling/002 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning.mp4 26.5 MB32 - Upper Confidence Bound (UCB)/009 Step 7 - Visualizing UCB Algorithm Results Histogram Analysis in Python.mp4 26.4 MB09 - Support Vector Regression (SVR)/001 How Does Support Vector Regression --(SVR--) Differ from Linear Regression.mp4 26.4 MB21 - Decision Tree Classification/001 How Decision Tree Algorithms Work Step-by-Step Guide with Examples.mp4 26.3 MB24 - Evaluating Classification Models Performance/001 Logistic Regression Interpreting Predictions and Errors in Data Science.mp4 25.8 MB27 - Hierarchical Clustering/001 How to Perform Hierarchical Clustering Step-by-Step Guide for Machine Learning.mp4 25.4 MB33 - Thompson Sampling/006 Step 4 - Beating UCB with Thompson Sampling Python Multi-Armed Bandit Tutorial.mp4 25.0 MB07 - Multiple Linear Regression/024 Mastering Feature Selection Backward Elimination in R for Linear Regression.mp4 24.5 MB32 - Upper Confidence Bound (UCB)/008 Step 6 - Reinforcement Learning Finalizing UCB Algorithm in Python.mp4 24.1 MB37 - Convolutional Neural Networks/014 Step 4 CNN Training - Epochs, Loss Function --& Metrics in TensorFlow.mp4 23.9 MB11 - Random Forest Regression/001 Understanding Random Forest Algorithm Intuition and Application in ML.mp4 23.8 MB07 - Multiple Linear Regression/004 How to Handle Categorical Variables in Linear Regression Models.mp4 23.8 MB32 - Upper Confidence Bound (UCB)/005 Step 3 - Python Code for Upper Confidence Bound Setting Up Key Variables.mp4 23.5 MB34 - -------------------- Part 7 Natural Language Processing --------------------/006 Step 1 - Getting Started with Natural Language Processing Sentiment Analysis.mp4 23.3 MB26 - K-Means Clustering/015 Step 5c - Analyzing Customer Segments Insights from K-means Clustering.mp4 22.7 MB34 - -------------------- Part 7 Natural Language Processing --------------------/007 Step 2 - Importing TSV Data for Sentiment Analysis Python NLP Data Processing.mp4 21.8 MB37 - Convolutional Neural Networks/004 Step 1b - Applying ReLU to Convolutional Layers Breaking Up Image Linearity.mp4 21.6 MB34 - -------------------- Part 7 Natural Language Processing --------------------/017 Step 3 - NLP in R Initialising a Corpus for Sentiment Analysis.mp4 21.5 MB36 - Artificial Neural Networks/016 Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R.mp4 21.1 MB21 - Decision Tree Classification/005 Step 2 - Decision Tree Classifier Optimizing Prediction Boundaries in R.mp4 20.4 MB24 - Evaluating Classification Models Performance/004 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio.mp4 20.4 MB17 - K-Nearest Neighbors (K-NN)/005 Step 1 - Implementing KNN Classification in R Setup --& Data Preparation.mp4 20.4 MB19 - Kernel SVM/008 Step 1 - Kernel SVM vs Linear SVM Overcoming Non-Linear Separability in R.mp4 20.3 MB18 - Support Vector Machine (SVM)/005 Step 1 - Building a Linear SVM Classifier in R Data Import and Initial Setup.mp4 20.2 MB06 - Simple Linear Regression/012 Step 2 - Fitting Simple Linear Regression in R LM Function and Model Summary.mp4 20.2 MB10 - Decision Tree Regression/008 Step 2 - Decision Tree Regression Fixing Splits with rpart Control Parameter.mp4 20.1 MB32 - Upper Confidence Bound (UCB)/007 Step 5 - Coding Upper Confidence Bound Optimizing Ad Selection in Python.mp4 20.1 MB18 - Support Vector Machine (SVM)/002 Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python.mp4 20.0 MB22 - Random Forest Classification/003 Step 2 Random Forest Evaluation - Confusion Matrix --& Accuracy Metrics.mp4 19.9 MB17 - K-Nearest Neighbors (K-NN)/002 Step 1 - Python KNN Tutorial Classifying Customer Data for Targeted Marketing.mp4 19.8 MB22 - Random Forest Classification/005 Step 2 Random Forest Classification - Visualizing Predictions --& Results.mp4 19.8 MB27 - Hierarchical Clustering/007 Step 2c - Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering.mp4 19.8 MB16 - Logistic Regression/024 Step 5b Logistic Regression - Linear Classifiers --& Prediction Boundaries.mp4 19.7 MB19 - Kernel SVM/007 Step 2 - Mastering Kernel SVM Improving Accuracy with Non-Linear Classifiers.mp4 19.7 MB04 - Data Preprocessing in R/005 Using R--'s Factor Function to Handle Categorical Variables in Data Analysis.mp4 19.6 MB20 - Naive Bayes/006 Step 2 - Python Naive Bayes Training and Evaluating a Classifier on Real Data.mp4 19.6 MB20 - Naive Bayes/003 Bayes Theorem in Machine Learning Step-by-Step Probability Calculation.mp4 19.5 MB16 - Logistic Regression/018 Step 1 - Data Preprocessing for Logistic Regression in R Preparing Your Dataset.mp4 19.5 MB19 - Kernel SVM/002 Support Vector Machines Transforming Non-Linear Data for Linear Separation.mp4 19.4 MB09 - Support Vector Regression (SVR)/012 Step 1 - SVR Tutorial Creating a Support Vector Machine Regressor in R.mp4 19.4 MB26 - K-Means Clustering/012 Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python.mp4 19.3 MB03 - Data Preprocessing in Python/017 Step 2 - Preparing Data Creating Training and Test Sets in Python for ML Models.mp4 19.3 MB21 - Decision Tree Classification/003 Step 2 - Training a Decision Tree Classifier Optimizing Performance in Python.mp4 19.3 MB21 - Decision Tree Classification/002 Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn.mp4 19.3 MB17 - K-Nearest Neighbors (K-NN)/004 Step 3 - Visualizing KNN Decision Boundaries Python Tutorial for Beginners.mp4 19.3 MB13 - Regression Model Selection in Python/003 Step 2 - Creating Generic Code Templates for Various Regression Models in Python.mp4 19.3 MB23 - Classification Model Selection in Python/004 Step 2 - Optimizing Model Selection Streamlined Classification Code in Python.mp4 19.3 MB26 - K-Means Clustering/016 Step 1 - K-Means Clustering in R Importing --& Exploring Segmentation Data.mp4 19.3 MB19 - Kernel SVM/006 Step 1 - Python Kernel SVM Applying RBF to Solve Non-Linear Classification.mp4 19.3 MB16 - Logistic Regression/009 Step 4a - Formatting Single Observation Input for Logistic Regression Predict.mp4 19.3 MB03 - Data Preprocessing in Python/010 Step 2 - Imputing Missing Data in Python SimpleImputer and Numerical Columns.mp4 19.3 MB20 - Naive Bayes/005 Step 1 - Naive Bayes in Python Applying ML to Social Network Ads Optimisation.mp4 19.3 MB06 - Simple Linear Regression/004 Step 1b Data Preprocessing for Linear Regression Import --& Split Data in Python.mp4 19.3 MB26 - K-Means Clustering/013 Step 5a Visualizing K-Means Clusters of Customer Data with Python Scatter.mp4 19.3 MB27 - Hierarchical Clustering/004 Step 1 - Getting Started with Hierarchical Clustering Data Setup in Python.mp4 19.3 MB27 - Hierarchical Clustering/006 Step 2b - Visualizing Hierarchical Clustering Dendrogram Basics in Python.mp4 19.2 MB09 - Support Vector Regression (SVR)/008 Step 3 SVM Regression Creating --& Training SVR Model with RBF Kernel in Python.mp4 19.2 MB08 - Polynomial Regression/019 Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R.mp4 19.2 MB22 - Random Forest Classification/002 Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn.mp4 19.2 MB08 - Polynomial Regression/006 Step 3a - Plotting Real vs Predicted Salaries Linear Regression Visualization.mp4 19.2 MB03 - Data Preprocessing in Python/020 Step 1 - Feature Scaling in ML Why It--'s Crucial for Data Preprocessing.mp4 19.2 MB16 - Logistic Regression/006 Step 2b - Data Preprocessing Feature Scaling Techniques for Logistic Regression.mp4 19.2 MB11 - Random Forest Regression/003 Step 2 - Creating a Random Forest Regressor Key Parameters and Model Fitting.mp4 19.2 MB21 - Decision Tree Classification/004 Step 1 - R Tutorial Creating a Decision Tree Classifier with rpart Library.mp4 19.2 MB22 - Random Forest Classification/004 Step 1 Random Forest Classifier - From Template to Implementation in R.mp4 19.2 MB04 - Data Preprocessing in R/004 How to Handle Missing Values in R Data Preprocessing for Machine Learning.mp4 19.2 MB11 - Random Forest Regression/004 Step 1 - Building a Random Forest Model in R Regression Tutorial.mp4 19.2 MB08 - Polynomial Regression/003 Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison.mp4 19.2 MB16 - Logistic Regression/023 Step 5a - Interpreting Logistic Regression Plots Prediction Regions Explained.mp4 19.2 MB03 - Data Preprocessing in Python/009 Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning.mp4 19.2 MB03 - Data Preprocessing in Python/023 Step 4 - Applying the Same Scaler to Training and Test Sets in Python.mp4 19.1 MB08 - Polynomial Regression/004 Step 2a Linear to Polynomial Regression - Preparing Data for Advanced Models.mp4 19.1 MB07 - Multiple Linear Regression/008 Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python.mp4 19.1 MB18 - Support Vector Machine (SVM)/003 Step 2 - Building a Support Vector Machine Model with Sklearn--'s SVC in Python.mp4 19.1 MB06 - Simple Linear Regression/014 Step 4a - Plotting Linear Regression Data in R ggplot2 Step-by-Step Guide.mp4 19.1 MB06 - Simple Linear Regression/009 Step 4b - Evaluating Linear Regression Model Performance on Test Data.mp4 19.0 MB03 - Data Preprocessing in Python/013 Step 2 - Handling Categorical Data One-Hot Encoding with ColumnTransformer.mp4 19.0 MB16 - Logistic Regression/014 Step 7a - Visualizing Logistic Regression Decision Boundaries in Python 2D Plot.mp4 19.0 MB11 - Random Forest Regression/002 Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn.mp4 19.0 MB16 - Logistic Regression/012 Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn.mp4 19.0 MB07 - Multiple Linear Regression/012 Step 3a - Scikit-learn for Multiple Linear Regression Efficient Model Building.mp4 19.0 MB23 - Classification Model Selection in Python/005 Step 3 - Evaluating Classification Algorithms Accuracy Metrics in Python.mp4 19.0 MB17 - K-Nearest Neighbors (K-NN)/003 Step 2 - Building a K-Nearest Neighbors Model Scikit-Learn KNeighborsClassifier.mp4 19.0 MB34 - -------------------- Part 7 Natural Language Processing --------------------/020 Step 6 - Cleaning Text Data Removing Punctuation for NLP and Classification.mp4 18.9 MB23 - Classification Model Selection in Python/003 Step 1 - How to Choose the Right Classification Algorithm for Your Dataset.mp4 18.9 MB16 - Logistic Regression/005 Step 2a Python Data Preprocessing for Logistic Regression Dataset Prep.mp4 18.9 MB06 - Simple Linear Regression/008 Step 4a - Linear Regression Plotting Real vs Predicted Salaries Visualization.mp4 18.9 MB26 - K-Means Clustering/009 Step 3a - Implementing the Elbow Method for K-Means Clustering in Python.mp4 18.9 MB33 - Thompson Sampling/003 Step 1 - Python Implementation of Thompson Sampling for Bandit Problems.mp4 18.8 MB36 - Artificial Neural Networks/009 Bank Customer Churn Prediction Machine Learning Model with TensorFlow.mp4 18.7 MB26 - K-Means Clustering/017 Step 2 - K-Means Algorithm Implementation in R Fitting and Analyzing Mall Data.mp4 18.7 MB09 - Support Vector Regression (SVR)/003 Step 1a - SVR Model Training Feature Scaling and Dataset Preparation in Python.mp4 18.7 MB03 - Data Preprocessing in Python/006 Step 3 - Preprocessing Data Building X and Y Vectors for ML Model Training.mp4 18.7 MB27 - Hierarchical Clustering/008 Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python.mp4 18.6 MB18 - Support Vector Machine (SVM)/006 Step 2 Creating --& Evaluating Linear SVM Classifier in R - Predictions --& Results.mp4 18.6 MB08 - Polynomial Regression/005 Step 2b - Transforming Linear to Polynomial Regression A Step-by-Step Guide.mp4 18.5 MB16 - Logistic Regression/003 Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction.mp4 18.5 MB27 - Hierarchical Clustering/009 Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering Python Example.mp4 18.5 MB26 - K-Means Clustering/010 Step 3b - Optimizing K-means Clustering WCSS and Elbow Method Implementation.mp4 18.5 MB21 - Decision Tree Classification/006 Step 3 - Decision Tree Visualization Exploring Splits and Conditions in R.mp4 18.4 MB19 - Kernel SVM/009 Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning.mp4 18.3 MB16 - Logistic Regression/020 Step 3 - How to Use R for Logistic Regression Prediction Step-by-Step Guide.mp4 18.3 MB01 - Welcome to the course! Here we will help you get started in the best conditions/004 How to Use Google Colab --& Machine Learning Course Folder.mp4 18.3 MB08 - Polynomial Regression/007 Step 3b - Polynomial vs Linear Regression Better Fit with Higher Degrees.mp4 18.3 MB07 - Multiple Linear Regression/014 Step 4a Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui.mp4 18.2 MB16 - Logistic Regression/011 Step 5 - Comparing Predicted vs Real Results Python Logistic Regression Guide.mp4 18.1 MB01 - Welcome to the course! Here we will help you get started in the best conditions/005 Getting Started with R Programming Install R and RStudio on Windows --& Mac.mp4 18.0 MB09 - Support Vector Regression (SVR)/005 Step 2a - Mastering Feature Scaling for Support Vector Regression in Python.mp4 18.0 MB07 - Multiple Linear Regression/015 Step 4b - ML in Python Evaluating Multiple Linear Regression Accuracy.mp4 18.0 MB39 - Principal Component Analysis (PCA)/003 Step 2 - PCA in Action Reducing Dimensions and Predicting Customer Segments.mp4 17.9 MB06 - Simple Linear Regression/015 Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2.mp4 17.8 MB07 - Multiple Linear Regression/020 Step 2a - Multiple Linear Regression in R Building --& Interpreting the Regressor.mp4 17.7 MB12 - Evaluating Regression Models Performance/002 Understanding Adjusted R-Squared Key Differences from R-Squared Explained.mp4 17.7 MB22 - Random Forest Classification/006 Step 3 - Evaluating Random Forest Performance Test Set Results --& Overfitting.mp4 17.7 MB11 - Random Forest Regression/006 Step 3 - Fine-Tuning Random Forest From 10 to 500 Trees for Accurate Prediction.mp4 17.6 MB11 - Random Forest Regression/005 Step 2 - Visualizing Random Forest Regression Interpreting Stairs and Splits.mp4 17.6 MB08 - Polynomial Regression/020 Step 2 - Mastering Regression Model Visualization Increasing Data Resolution.mp4 17.6 MB04 - Data Preprocessing in R/010 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models.mp4 17.6 MB26 - K-Means Clustering/008 Step 2b K-Means Clustering - Optimizing Features for 2D Visualization.mp4 17.5 MB16 - Logistic Regression/027 Optimizing R Scripts for Machine Learning Building a Classification Template.mp4 17.4 MB36 - Artificial Neural Networks/008 Deep Learning Fundamentals Training Neural Networks Step-by-Step.mp4 17.3 MB03 - Data Preprocessing in Python/002 Step 2 - Data Preprocessing Techniques From Raw Data to ML-Ready Datasets.mp4 17.3 MB34 - -------------------- Part 7 Natural Language Processing --------------------/022 Step 8 - Enhancing Text Classification Stemming for Efficient Feature Matrices.mp4 17.3 MB08 - Polynomial Regression/016 Step 3c - Polynomial Regression Curve Fitting for Better Predictions.mp4 17.3 MB03 - Data Preprocessing in Python/001 Step 1 - Data Preprocessing in Python Preparing Your Dataset for ML Models.mp4 17.3 MB02 - -------------------- Part 1 Data Preprocessing --------------------/004 Feature Scaling in Machine Learning Normalization vs Standardization Explained.mp4 17.1 MB16 - Logistic Regression/025 Step 5c - Data Viz in R Colorizing Pixels for Logistic Regression.mp4 17.1 MB08 - Polynomial Regression/015 Step 3b Visualizing Linear Regression - Plotting Predictions vs Observations.mp4 17.0 MB27 - Hierarchical Clustering/011 Step 2 Using H.clust in R - Building --& Interpreting Dendrograms for Clustering.mp4 17.0 MB03 - Data Preprocessing in Python/004 Step 1 - Machine Learning Basics Importing Datasets Using Pandas read_csv--(--).mp4 16.9 MB30 - Eclat/001 Mastering ECLAT Support-Based Approach to Market Basket Optimization.mp4 16.8 MB19 - Kernel SVM/010 Step 3 Visualizing Kernel SVM - Non-Linear Classification in Machine Learning.mp4 16.7 MB08 - Polynomial Regression/001 Understanding Polynomial Linear Regression Applications and Examples.mp4 16.6 MB22 - Random Forest Classification/001 Understanding Random Forest Decision Trees and Majority Voting Explained.mp4 16.5 MB06 - Simple Linear Regression/003 Step 1a - Mastering Simple Linear Regression Key Concepts and Implementation.mp4 16.3 MB04 - Data Preprocessing in R/009 How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2.mp4 16.2 MB08 - Polynomial Regression/014 Step 3a Visualizing Regression Results - Creating Scatter Plots with ggplot2 in.mp4 16.2 MB08 - Polynomial Regression/013 Step 2b - Building a Polynomial Regression Model Adding Squared --& Cubed Terms.mp4 16.1 MB10 - Decision Tree Regression/006 Step 4 - Visualizing Decision Tree Regression High-Resolution Results.mp4 16.1 MB10 - Decision Tree Regression/004 Step 2 - Implementing DecisionTreeRegressor A Step-by-Step Guide in Python.mp4 16.1 MB09 - Support Vector Regression (SVR)/006 Step 2b Reshaping Data for SVR - Preparing Y Vector for Feature Scaling --(Python.mp4 16.0 MB10 - Decision Tree Regression/007 Step 1 - Creating a Decision Tree Regressor Using rpart Function in R.mp4 16.0 MB26 - K-Means Clustering/014 Step 5b - Visualizing K-Means Clusters Plotting Customer Segments in Python.mp4 16.0 MB04 - Data Preprocessing in R/007 Step 2 - Preparing Data Creating Training and Test Sets in R for ML Models.mp4 15.9 MB26 - K-Means Clustering/007 Step 2a - K-Means Clustering in Python Selecting Relevant Features for Analysis.mp4 15.9 MB20 - Naive Bayes/008 Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification.mp4 15.8 MB43 - Model Selection/002 How to Master the Bias-Variance Tradeoff in Machine Learning Models.mp4 15.8 MB17 - K-Nearest Neighbors (K-NN)/007 Step 3 - Implementing KNN Classification in R Adapting the Classifier Template.mp4 15.7 MB27 - Hierarchical Clustering/005 Step 2a - Implementing Hierarchical Clustering Building a Dendrogram with SciPy.mp4 15.7 MB17 - K-Nearest Neighbors (K-NN)/001 K-Nearest Neighbors --(KNN--) Explained A Beginner--'s Guide to Classification.mp4 15.7 MB08 - Polynomial Regression/012 Step 2a - Building Linear --& Polynomial Regression Models in R A Comparison.mp4 15.7 MB20 - Naive Bayes/009 Step 2 - Troubleshooting Naive Bayes Classification Empty Prediction Vectors.mp4 15.6 MB13 - Regression Model Selection in Python/006 Step 1 - Selecting the Best Regression Model R-squared Evaluation in Python.mp4 15.5 MB03 - Data Preprocessing in Python/021 Step 2 - How to Scale Numeric Features in Python for ML Preprocessing.mp4 15.3 MB13 - Regression Model Selection in Python/002 Step 1 - Mastering Regression Toolkit Comparing Models for Optimal Performance.mp4 15.3 MB07 - Multiple Linear Regression/011 Step 2b - Multiple Linear Regression in Python Preparing Your Dataset.mp4 15.2 MB03 - Data Preprocessing in Python/005 Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing.mp4 15.2 MB06 - Simple Linear Regression/011 Step 1 - Data Preprocessing in R Preparing for Linear Regression Modeling.mp4 15.2 MB10 - Decision Tree Regression/002 Step 1a - Decision Tree Regression Building a Model without Feature Scaling.mp4 15.1 MB01 - Welcome to the course! Here we will help you get started in the best conditions/002 Get Excited about ML Predict Car Purchases with Python --& Scikit-learn in 5 mins.mp4 15.1 MB03 - Data Preprocessing in Python/014 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 15.1 MB04 - Data Preprocessing in R/006 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4 15.0 MB17 - K-Nearest Neighbors (K-NN)/006 Step 2 - Building a KNN Classifier Preparing Training and Test Sets in R.mp4 14.9 MB07 - Multiple Linear Regression/013 Step 3b - Scikit-Learn Building --& Training Multiple Linear Regression Models.mp4 14.9 MB06 - Simple Linear Regression/007 Step 3 - Using Scikit-Learn--'s Predict Method for Linear Regression in Python.mp4 14.8 MB07 - Multiple Linear Regression/022 Step 3 - How to Use predict--(--) Function in R for Multiple Linear Regression.mp4 14.6 MB10 - Decision Tree Regression/009 Step 3 Non-Continuous Regression - Decision Tree Visualization Challenges.mp4 14.5 MB07 - Multiple Linear Regression/010 Step 2a - Hands-on Multiple Linear Regression Preparing Data in Python.mp4 14.5 MB04 - Data Preprocessing in R/008 Feature Scaling in ML Step 1 Why It--'s Crucial for Data Preprocessing.mp4 14.3 MB03 - Data Preprocessing in Python/012 Step 1 - One-Hot Encoding Transforming Categorical Features for ML Algorithms.mp4 14.3 MB07 - Multiple Linear Regression/021 Step 2b Statistical Significance - P-values --& Stars in Regression.mp4 14.1 MB06 - Simple Linear Regression/016 Step 4c - Comparing Training vs Test Set Predictions in Linear Regression.mp4 14.0 MB37 - Convolutional Neural Networks/008 Deep Learning Basics How Convolutional Neural Networks --(CNNs--) Process Images.mp4 14.0 MB26 - K-Means Clustering/004 K-Means++ Algorithm Solving the Random Initialization Trap in Clustering.mp4 13.8 MB07 - Multiple Linear Regression/003 Understanding Linear Regression Assumptions Linearity, Homoscedasticity --& More.mp4 13.7 MB13 - Regression Model Selection in Python/007 Step 2 - Selecting the Best Regression Model Random Forest vs. SVR Performance.mp4 13.6 MB34 - -------------------- Part 7 Natural Language Processing --------------------/003 Deep NLP --& Sequence-to-Sequence Models Exploring Natural Language Processing.mp4 13.5 MB07 - Multiple Linear Regression/019 Step 1b - Preparing Datasets for Multiple Linear Regression in R.mp4 12.9 MB23 - Classification Model Selection in Python/002 Mastering the Confusion Matrix True Positives, Negatives, and Errors.mp4 12.9 MB16 - Logistic Regression/004 Step 1b - Implementing Logistic Regression in Python Data Preprocessing Guide.mp4 12.9 MB08 - Polynomial Regression/017 Step 4a - How to Make Single Predictions Using Polynomial Regression in R.mp4 12.9 MB08 - Polynomial Regression/008 Step 4a Predicting Salaries - Linear Regression in Python --(Array Input Guide--).mp4 12.9 MB13 - Regression Model Selection in Python/004 Step 3 Evaluating Regression Models - R-Squared --& Performance Metrics Explained.mp4 12.9 MB06 - Simple Linear Regression/006 Step 2b - Machine Learning Basics Training a Linear Regression Model in Python.mp4 12.8 MB26 - K-Means Clustering/011 Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python.mp4 12.8 MB10 - Decision Tree Regression/003 Step 1b Uploading --& Preprocessing Data for Decision Tree Regression in Python.mp4 12.8 MB16 - Logistic Regression/007 Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn.mp4 12.8 MB13 - Regression Model Selection in Python/005 Step 4 - Implementing R-Squared Score in Python with Scikit-Learn--'s Metrics.mp4 12.8 MB09 - Support Vector Regression (SVR)/013 Step 2 - Support Vector Regression Building a Predictive Model in Python.mp4 12.7 MB03 - Data Preprocessing in Python/016 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.mp4 12.6 MB10 - Decision Tree Regression/010 Step 4 - Visualizing Decision Tree Understanding Intervals and Predictions.mp4 12.6 MB07 - Multiple Linear Regression/018 Step 1a - Data Preprocessing for MLR Handling Categorical Data.mp4 12.6 MB06 - Simple Linear Regression/005 Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python.mp4 12.6 MB03 - Data Preprocessing in Python/018 Step 3 - Splitting Data into Training and Test Sets Best Practices in Python.mp4 12.5 MB32 - Upper Confidence Bound (UCB)/004 Step 2 Implementing UCB Algorithm in Python - Data Preparation.mp4 12.5 MB26 - K-Means Clustering/005 Step 1a - Python K-Means Tutorial Identifying Customer Patterns in Mall Data.mp4 12.5 MB08 - Polynomial Regression/002 Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python.mp4 12.5 MB40 - Linear Discriminant Analysis (LDA)/001 LDA Intuition Maximizing Class Separation in Machine Learning Algorithms.mp4 12.4 MB03 - Data Preprocessing in Python/022 Step 3 - Implementing Feature Scaling Fit and Transform Methods Explained.mp4 12.3 MB27 - Hierarchical Clustering/010 Step 1 - R Data Import for Clustering Annual Income --& Spending Score Analysis.mp4 12.3 MB09 - Support Vector Regression (SVR)/009 Step 4 - SVR Model Prediction Handling Scaled Data and Inverse Transformation.mp4 12.3 MB08 - Polynomial Regression/018 Step 4b - Predicting Salaries with Polynomial Regression A Practical Example.mp4 12.3 MB07 - Multiple Linear Regression/001 Startup Success Prediction Regression Model for VC Fund Decision-Making.mp4 12.2 MB08 - Polynomial Regression/010 Step 1a - Implementing Polynomial Regression in R HR Salary Analysis Case Study.mp4 12.1 MB06 - Simple Linear Regression/013 Step 3 - How to Use predict--(--) Function in R for Linear Regression Analysis.mp4 12.1 MB16 - Logistic Regression/015 Step 7b - Interpreting Logistic Regression Results Prediction Regions Explained.mp4 12.1 MB08 - Polynomial Regression/011 Step 1b - ML Fundamentals Preparing Data for Polynomial Regression.mp4 12.0 MB26 - K-Means Clustering/003 How to Use the Elbow Method in K-Means Clustering A Step-by-Step Guide.mp4 12.0 MB09 - Support Vector Regression (SVR)/010 Step 5a - How to Plot Support Vector Regression --(SVR--) Models Step-by-Step Guide.mp4 12.0 MB20 - Naive Bayes/010 Step 3 - Visualizing Naive Bayes Results Creating Confusion Matrix and Graphs.mp4 11.9 MB09 - Support Vector Regression (SVR)/011 Step 5b - SVR Scaling --& Inverse Transformation in Python.mp4 11.9 MB08 - Polynomial Regression/009 Step 4b Python Polynomial Regression - Predicting Salaries Accurately.mp4 11.8 MB16 - Logistic Regression/001 Understanding Logistic Regression Predicting Categorical Outcomes.mp4 11.6 MB39 - Principal Component Analysis (PCA)/001 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning.mp4 11.6 MB03 - Data Preprocessing in Python/003 Machine Learning Toolkit Importing NumPy, Matplotlib, and Pandas Libraries.mp4 11.5 MB09 - Support Vector Regression (SVR)/007 Step 2c SVR Data Prep - Scaling X --& Y Independently with StandardScaler.mp4 11.4 MB16 - Logistic Regression/008 Step 3b - Training Logistic Regression Model Fit Method for Classification.mp4 11.3 MB09 - Support Vector Regression (SVR)/004 Step 1b - SVR in Python Importing Libraries and Dataset for Machine Learning.mp4 11.3 MB09 - Support Vector Regression (SVR)/002 RBF Kernel SVR From Linear to Non-Linear Support Vector Regression.mp4 11.2 MB34 - -------------------- Part 7 Natural Language Processing --------------------/021 Step 7 - Simplifying Corpus Using SnowballC Package to Remove Stop Words in R.mp4 11.1 MB16 - Logistic Regression/013 Step 6b Evaluating Classification Models - Confusion Matrix --& Accuracy Metrics.mp4 11.1 MB37 - Convolutional Neural Networks/001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4 11.0 MB27 - Hierarchical Clustering/012 Step 3 - Implementing Hierarchical Clustering Using Cat Tree Method in R.mp4 10.8 MB16 - Logistic Regression/016 Step 7c - Visualizing Logistic Regression Performance on New Data in Python.mp4 10.8 MB19 - Kernel SVM/001 From Linear to Non-Linear SVM Exploring Higher Dimensional Spaces.mp4 10.6 MB10 - Decision Tree Regression/005 Step 3 - Implementing Decision Tree Regression in Python Making Predictions.mp4 10.6 MB16 - Logistic Regression/019 Step 2 - How to Create a Logistic Regression Classifier Using R--'s GLM Function.mp4 10.5 MB27 - Hierarchical Clustering/013 Step 4 - Cluster Plot Method Visualizing Hierarchical Clustering Results in R.mp4 10.1 MB16 - Logistic Regression/002 Logistic Regression Finding the Best Fit Curve Using Maximum Likelihood.mp4 10.1 MB34 - -------------------- Part 7 Natural Language Processing --------------------/018 Step 4 - NLP Data Cleaning Lowercase Transformation in R for Text Analysis.mp4 9.9 MB33 - Thompson Sampling/009 Step 2 - Reinforcement Learning Thompson Sampling Outperforms UCB Algorithm.mp4 9.9 MB32 - Upper Confidence Bound (UCB)/013 Step 4 - UCB Algorithm Performance Analyzing Ad Selection with Histograms.mp4 9.8 MB34 - -------------------- Part 7 Natural Language Processing --------------------/002 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning.mp4 9.7 MB26 - K-Means Clustering/006 Step 1b K-Means Clustering - Data Preparation in Google ColabJupyter.mp4 9.6 MB16 - Logistic Regression/021 Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R.mp4 9.1 MB04 - Data Preprocessing in R/003 R Tutorial Importing and Viewing Datasets for Data Preprocessing.mp4 8.9 MB18 - Support Vector Machine (SVM)/004 Step 3 - Understanding Linear SVM Limitations Why It Didn--'t Beat kNN Classifier.mp4 8.7 MB23 - Classification Model Selection in Python/006 Step 4 - Model Selection Process Evaluating Classification Algorithms.mp4 8.6 MB27 - Hierarchical Clustering/014 Step 5 - Hierarchical Clustering in R Understanding Customer Spending Patterns.mp4 8.5 MB07 - Multiple Linear Regression/009 Step 1b - Hands-On Guide Implementing Multiple Linear Regression in Python.mp4 8.4 MB12 - Evaluating Regression Models Performance/001 Understanding R-squared Evaluating Goodness of Fit in Regression Models.mp4 8.3 MB36 - Artificial Neural Networks/001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.mp4 8.3 MB15 - -------------------- Part 3 Classification --------------------/002 What is Classification in Machine Learning Fundamentals and Applications.mp4 8.1 MB07 - Multiple Linear Regression/002 Multiple Linear Regression Independent Variables --& Prediction Models.mp4 7.9 MB19 - Kernel SVM/004 Understanding Different Types of Kernel Functions for Machine Learning.mp4 7.8 MB06 - Simple Linear Regression/001 Simple Linear Regression Understanding the Equation and Potato Yield Prediction.mp4 7.7 MB26 - K-Means Clustering/001 What is Clustering in Machine Learning Introduction to Unsupervised Learning.mp4 7.3 MB24 - Evaluating Classification Models Performance/002 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics.mp4 7.2 MB07 - Multiple Linear Regression/005 Multicollinearity in Regression Understanding the Dummy Variable Trap.mp4 7.1 MB34 - -------------------- Part 7 Natural Language Processing --------------------/019 Step 5 - Sentiment Analysis Data Cleaning Removing Numbers with TM Map.mp4 6.8 MB02 - -------------------- Part 1 Data Preprocessing --------------------/003 Data Preprocessing Importance of Training-Test Split in ML Model Evaluation.mp4 6.6 MB04 - Data Preprocessing in R/002 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables.mp4 6.3 MB06 - Simple Linear Regression/002 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression.mp4 6.3 MB37 - Convolutional Neural Networks/006 Step 3 - Understanding Flattening in Convolutional Neural Network Architecture.mp4 6.1 MB26 - K-Means Clustering/002 K-Means Clustering Tutorial Visualizing the Machine Learning Algorithm.mp4 5.9 MB16 - Logistic Regression/010 Step 4b Predicted vs. Real Purchase Decisions in Logistic Regression.mp4 5.9 MB20 - Naive Bayes/007 Step 3 - Analyzing Naive Bayes Algorithm Results Accuracy and Predictions.mp4 5.2 MB04 - Data Preprocessing in R/001 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning.mp4 5.2 MB02 - -------------------- Part 1 Data Preprocessing --------------------/002 Machine Learning Workflow Importing, Modeling, and Evaluating Your ML Model.mp4 3.8 MB13 - Regression Model Selection in Python/008 Regression-Bonus.zip 373.2 kB14 - Regression Model Selection in R/003 Regression-Bonus.zip 373.2 kB13 - Regression Model Selection in Python/001 Machine-Learning-A-Z-Model-Selection.zip 165.8 kB23 - Classification Model Selection in Python/001 Machine-Learning-A-Z-Model-Selection.zip 163.8 kB
03 - Data Preprocessing in Python/024 Coding exercise 5 Feature scaling for Machine Learning.html 94.0 kB30 - Eclat/003 Eclat.zip 49.7 kB
43 - Model Selection/004 Optimizing SVM Models with GridSearchCV A Step-by-Step Python Tutorial.srt 44.6 kB37 - Convolutional Neural Networks/016 Hands-on CNN Training Using Jupyter Notebook for Image Classification.srt 38.4 kB20 - Naive Bayes/001 Understanding Bayes--' Theorem Intuitively From Probability to Machine Learning.srt 37.8 kB37 - Convolutional Neural Networks/013 Step 3 - TensorFlow CNN Convolution to Output Layer for Vision Tasks.srt 37.5 kB41 - Kernel PCA/002 Implementing Kernel PCA for Non-Linear Data Step-by-Step Guide.srt 37.2 kB29 - Apriori/003 Step 2 - Creating a List of Transactions for Market Basket Analysis in Python.srt 36.2 kB39 - Principal Component Analysis (PCA)/002 Step 1 PCA in Python Reducing Wine Dataset Features with Scikit-learn.srt 35.4 kB29 - Apriori/005 Step 4 Visualizing Apriori Algorithm Results for Product Deals in Python.srt 35.1 kB37 - Convolutional Neural Networks/007 Step 4 - Fully Connected Layers in CNNs Optimizing Feature Combination.srt 34.8 kB40 - Linear Discriminant Analysis (LDA)/003 Step-by-Step Guide Applying LDA for Feature Extraction in Machine Learning.srt 34.4 kB33 - Thompson Sampling/001 Understanding Thompson Sampling Algorithm Intuition and Implementation.srt 34.2 kB29 - Apriori/008 Step 3 Optimizing Product Placement - Apriori Algorithm, Lift --& Confidence.srt 34.0 kB32 - Upper Confidence Bound (UCB)/006 Step 4 - Python for RL Coding the UCB Algorithm Step-by-Step.srt 33.8 kB29 - Apriori/006 Step 1 - Creating a Sparse Matrix for Association Rule Mining in R.srt 33.6 kB03 - Data Preprocessing in Python/011 Coding Exercise 2 Handling Missing Data in a Dataset for Machine Learning.html 33.5 kB37 - Convolutional Neural Networks/009 Deep Learning Essentials Understanding Softmax and Cross-Entropy in CNNs.srt 32.8 kB43 - Model Selection/005 Evaluating ML Model Accuracy K-Fold Cross-Validation Implementation in R.srt 32.8 kB33 - Thompson Sampling/008 Step 1 - Thompson Sampling vs UCB Optimizing Ad Click-Through Rates in R.srt 32.5 kB36 - Artificial Neural Networks/011 Step 2 - TensorFlow 2.0 Tutorial Preprocessing Data for Customer Churn Model.srt 32.2 kB36 - Artificial Neural Networks/002 Deep Learning Basics Exploring Neurons, Synapses, and Activation Functions.srt 32.2 kB37 - Convolutional Neural Networks/012 Step 2 - Keras ImageDataGenerator Prevent Overfitting in CNN Models.srt 31.3 kB34 - -------------------- Part 7 Natural Language Processing --------------------/024 Step 10 - Building a Document-Term Matrix for NLP Text Classification.srt 31.2 kB29 - Apriori/001 Apriori Algorithm Uncovering Hidden Patterns in Data Mining Association Rules.srt 31.1 kB32 - Upper Confidence Bound (UCB)/011 Step 2 - UCB Algorithm in R Calculating Average Reward --& Confidence Interval.srt 31.1 kB44 - XGBoost/001 How to Use XGBoost in Python for Cancer Prediction with High Accuracy.srt 30.7 kB44 - XGBoost/003 XGBoost Tutorial Implementing Gradient Boosting for Classification Problems.srt 30.6 kB36 - Artificial Neural Networks/015 Step 1 - How to Preprocess Data for Artificial Neural Networks in R.srt 30.6 kB07 - Multiple Linear Regression/023 Optimizing Multiple Regression Models Backward Elimination Technique in R.srt 30.5 kB07 - Multiple Linear Regression/007 Backward Elimination Building Robust Multiple Linear Regression Models.srt 30.5 kB34 - -------------------- Part 7 Natural Language Processing --------------------/010 Step 5 - Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis.srt 30.3 kB40 - Linear Discriminant Analysis (LDA)/002 Mastering Linear Discriminant Analysis Step-by-Step Python Implementation.srt 30.2 kB37 - Convolutional Neural Networks/015 Step 5 - Making Single Predictions with Convolutional Neural Networks in Python.srt 30.2 kB
24 - Evaluating Classification Models Performance/005 Classification-Pros-Cons.pdf 30.0 kB32 - Upper Confidence Bound (UCB)/012 Step 3 Optimizing Ad Selection - UCB --& Multi-Armed Bandit Algorithm Explained.srt 29.4 kB37 - Convolutional Neural Networks/003 Step 1 - Understanding Convolution in CNNs Feature Detection and Feature Maps.srt 29.4 kB33 - Thompson Sampling/005 Step 3 - Python Code for Thompson Sampling Maximizing Random Beta Distributions.srt 29.4 kB34 - -------------------- Part 7 Natural Language Processing --------------------/005 Implementing Bag of Words in NLP A Step-by-Step Tutorial.srt 29.2 kB45 - Annex Logistic Regression (Long Explanation)/001 Logistic Regression Intuition.srt 29.0 kB36 - Artificial Neural Networks/018 Step 4 - H2O Deep Learning Making Predictions and Evaluating Model Accuracy.srt 28.5 kB32 - Upper Confidence Bound (UCB)/003 Step 1 - Upper Confidence Bound Solving Multi-Armed Bandit Problem in Python.srt 28.4 kB34 - -------------------- Part 7 Natural Language Processing --------------------/014 Step 1 - Text Classification Using Bag-of-Words and Random Forest in R.srt 28.2 kB32 - Upper Confidence Bound (UCB)/010 Step 1 - Exploring Upper Confidence Bound in R Multi-Armed Bandit Problems.srt 28.0 kB20 - Naive Bayes/002 Understanding Naive Bayes Algorithm Probabilistic Classification Explained.srt 27.9 kB43 - Model Selection/003 K-Fold Cross-Validation in Python Improve Machine Learning Model Performance.srt 27.8 kB36 - Artificial Neural Networks/014 Step 5 - Implementing ANN for Churn Prediction From Model to Confusion Matrix.srt 27.2 kB32 - Upper Confidence Bound (UCB)/002 Upper Confidence Bound Algorithm Solving Multi-Armed Bandit Problems in ML.srt 27.2 kB34 - -------------------- Part 7 Natural Language Processing --------------------/008 Step 3 - Text Cleaning for NLP Remove Punctuation and Convert to Lowercase.srt 27.1 kB37 - Convolutional Neural Networks/002 Introduction to CNNs Understanding Deep Learning for Computer Vision.srt 26.9 kB32 - Upper Confidence Bound (UCB)/001 Multi-Armed Bandit Exploration vs Exploitation in Reinforcement Learning.srt 26.7 kB29 - Apriori/004 Step 3 - Configuring Apriori Function Support, Confidence, and Lift in Python.srt 26.5 kB27 - Hierarchical Clustering/016 Clustering-Pros-Cons.pdf 26.4 kB37 - Convolutional Neural Networks/005 Step 2 - Max Pooling in CNNs Enhancing Spatial Invariance for Image Recognition.srt 25.9 kB29 - Apriori/007 Step 2 - Optimizing Apriori Model Choosing Minimum Support and Confidence.srt 25.5 kB30 - Eclat/002 Python Tutorial Adapting Apriori to Eclat for Efficient Frequent Itemset Mining.srt 25.5 kB36 - Artificial Neural Networks/012 Step 3 - Designing ANN Sequential Model --& Dense Layers for Deep Learning.srt 25.1 kB43 - Model Selection/006 Optimizing SVM Models with Grid Search A Step-by-Step R Tutorial.srt 24.3 kB36 - Artificial Neural Networks/017 Step 3 Building Deep Learning Model - H2O Neural Network Layer Config.srt 24.2 kB36 - Artificial Neural Networks/004 How Do Neural Networks Work Step-by-Step Guide to Deep Learning Algorithms.srt 23.5 kB34 - -------------------- Part 7 Natural Language Processing --------------------/009 Step 4 - Text Preprocessing Stemming and Stop Word Removal for NLP in Python.srt 23.4 kB03 - Data Preprocessing in Python/015 Coding Exercise 3 Encoding Categorical Data for Machine Learning.html 23.3 kB39 - Principal Component Analysis (PCA)/006 Step 3 - Implementing PCA and SVM for Customer Segmentation Practical Guide.srt 23.2 kB34 - -------------------- Part 7 Natural Language Processing --------------------/023 Step 9 Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning.srt 23.0 kB36 - Artificial Neural Networks/005 How Do Neural Networks Learn Deep Learning Fundamentals Explained.srt 22.3 kB41 - Kernel PCA/001 Kernel PCA in Python Improving Classification Accuracy with Feature Extraction.srt 22.2 kB39 - Principal Component Analysis (PCA)/004 Step 1 in R - Understanding Principal Component Analysis for Feature Extraction.srt 22.1 kB20 - Naive Bayes/011 Naive Bayes Quiz.html 21.8 kB35 - -------------------- Part 8 Deep Learning --------------------/002 Introduction to Deep Learning From Historical Context to Modern Applications.srt 21.6 kB04 - Data Preprocessing in R/011 Data Preprocessing Quiz.html 21.4 kB19 - Kernel SVM/011 Kernel SVM Quiz.html 21.4 kB07 - Multiple Linear Regression/026 Multiple Linear Regression Quiz.html 21.1 kB18 - Support Vector Machine (SVM)/007 SVM Quiz.html 21.1 kB22 - Random Forest Classification/007 Random Forest Classification Quiz.html 21.1 kB24 - Evaluating Classification Models Performance/006 Evaluating Classiification Model Performance Quiz.html 21.0 kB06 - Simple Linear Regression/017 Simple Linear Regression Quiz.html 21.0 kB16 - Logistic Regression/029 Logistic Regression Quiz.html 21.0 kB08 - Polynomial Regression/021 Polynomial Regression Quiz.html 21.0 kB09 - Support Vector Regression (SVR)/014 SVR Quiz.html 20.9 kB21 - Decision Tree Classification/007 Decision Tree Classification Quiz.html 20.9 kB11 - Random Forest Regression/007 Random Forest Regression Quiz.html 20.9 kB07 - Multiple Linear Regression/006 Understanding P-Values and Statistical Significance in Hypothesis Testing.srt 20.8 kB12 - Evaluating Regression Models Performance/003 Evaluating Regression Models Performance Quiz.html 20.8 kB36 - Artificial Neural Networks/013 Step 4 - Train Neural Network Compile --& Fit for Customer Churn Prediction.srt 20.8 kB29 - Apriori/009 Apriori Quiz.html 20.8 kB33 - Thompson Sampling/010 Thompson Sampling Quiz.html 20.7 kB27 - Hierarchical Clustering/015 Hierarchical Clustering Quiz.html 20.7 kB32 - Upper Confidence Bound (UCB)/014 Upper Confidence Bound Quiz.html 20.7 kB40 - Linear Discriminant Analysis (LDA)/004 LDA Quiz.html 20.7 kB39 - Principal Component Analysis (PCA)/007 PCA Quiz.html 20.7 kB35 - -------------------- Part 8 Deep Learning --------------------/003 Deep Learning Quiz.html 20.7 kB26 - K-Means Clustering/018 K-Means Clustering Quiz.html 20.7 kB10 - Decision Tree Regression/011 Decision Tree Regression Quiz.html 20.7 kB36 - Artificial Neural Networks/021 ANN QUIZ.html 20.6 kB34 - -------------------- Part 7 Natural Language Processing --------------------/026 Natural Language Processing Quiz.html 20.6 kB30 - Eclat/004 Eclat Quiz.html 20.6 kB37 - Convolutional Neural Networks/018 CNN Quiz.html 20.6 kB33 - Thompson Sampling/004 Step 2 - Optimizing Ad Selection with Thompson Sampling Algorithm in Python.srt 20.6 kB17 - K-Nearest Neighbors (K-NN)/008 K-Nearest Neighbor Quiz.html 20.6 kB39 - Principal Component Analysis (PCA)/005 Step 2 - Using preProcess Function in R for PCA Extracting Principal Components.srt 19.8 kB27 - Hierarchical Clustering/003 Mastering Hierarchical Clustering Dendrogram Analysis and Threshold Setting.srt 19.8 kB19 - Kernel SVM/003 Kernel Trick SVM Machine Learning for Non-Linear Classification.srt 19.8 kB37 - Convolutional Neural Networks/011 Step 1 Intro to CNNs for Image Classification.srt 19.6 kB10 - Decision Tree Regression/001 How to Build a Regression Tree Step-by-Step Guide for Machine Learning.srt 19.1 kB19 - Kernel SVM/005 Mastering Support Vector Regression Non-Linear SVR with RBF Kernel Explained.srt 18.9 kB24 - Evaluating Classification Models Performance/003 Understanding CAP Curves Assessing Model Performance in Data Science 2024.srt 18.7 kB36 - Artificial Neural Networks/010 Step 1 ANN in Python Predicting Customer Churn with TensorFlow.srt 18.3 kB34 - -------------------- Part 7 Natural Language Processing --------------------/004 From IfElse Rules to CNNs Evolution of Natural Language Processing.srt 18.2 kB36 - Artificial Neural Networks/006 Deep Learning Fundamentals Gradient Descent vs Brute Force Optimization.srt 18.0 kB20 - Naive Bayes/004 Why is Naive Bayes Called Naive Understanding the Algorithm--'s Assumptions.srt 17.8 kB18 - Support Vector Machine (SVM)/001 Support Vector Machines Explained Hyperplanes and Support Vectors in ML.srt 17.7 kB34 - -------------------- Part 7 Natural Language Processing --------------------/011 Step 6 - Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis.srt 17.7 kB30 - Eclat/003 Eclat vs Apriori Simplified Association Rule Learning in Data Mining.srt 17.6 kB43 - Model Selection/001 Mastering Model Evaluation K-Fold Cross-Validation Techniques Explained.srt 16.8 kB27 - Hierarchical Clustering/001 How to Perform Hierarchical Clustering Step-by-Step Guide for Machine Learning.srt 16.4 kB27 - Hierarchical Clustering/002 Visualizing Cluster Dissimilarity Dendrograms in Hierarchical Clustering.srt 16.2 kB29 - Apriori/002 Step 1 - Association Rule Learning Boost Sales with Python Data Mining.srt 16.1 kB14 - Regression Model Selection in R/002 Linear Regression Analysis Interpreting Coefficients for Business Decisions.srt 15.4 kB36 - Artificial Neural Networks/007 Stochastic vs Batch Gradient Descent Deep Learning Fundamentals.srt 15.2 kB34 - -------------------- Part 7 Natural Language Processing --------------------/016 Step 2 - NLP Data Preprocessing in R Importing TSV Files for Sentiment Analysis.srt 15.2 kB21 - Decision Tree Classification/001 How Decision Tree Algorithms Work Step-by-Step Guide with Examples.srt 15.2 kB32 - Upper Confidence Bound (UCB)/008 Step 6 - Reinforcement Learning Finalizing UCB Algorithm in Python.srt 15.1 kB07 - Multiple Linear Regression/024 Mastering Feature Selection Backward Elimination in R for Linear Regression.srt 14.9 kB14 - Regression Model Selection in R/001 Optimizing Regression Models R-Squared vs Adjusted R-Squared Explained.srt 14.7 kB36 - Artificial Neural Networks/003 Neural Network Basics Understanding Activation Functions in Deep Learning.srt 14.5 kB32 - Upper Confidence Bound (UCB)/005 Step 3 - Python Code for Upper Confidence Bound Setting Up Key Variables.srt 14.3 kB09 - Support Vector Regression (SVR)/001 How Does Support Vector Regression --(SVR--) Differ from Linear Regression.srt 14.0 kB33 - Thompson Sampling/002 Deterministic vs Probabilistic UCB and Thompson Sampling in Machine Learning.srt 13.9 kB34 - -------------------- Part 7 Natural Language Processing --------------------/007 Step 2 - Importing TSV Data for Sentiment Analysis Python NLP Data Processing.srt 13.8 kB32 - Upper Confidence Bound (UCB)/009 Step 7 - Visualizing UCB Algorithm Results Histogram Analysis in Python.srt 13.2 kB19 - Kernel SVM/002 Support Vector Machines Transforming Non-Linear Data for Linear Separation.srt 12.9 kB24 - Evaluating Classification Models Performance/001 Logistic Regression Interpreting Predictions and Errors in Data Science.srt 12.9 kB34 - -------------------- Part 7 Natural Language Processing --------------------/006 Step 1 - Getting Started with Natural Language Processing Sentiment Analysis.srt 12.8 kB32 - Upper Confidence Bound (UCB)/007 Step 5 - Coding Upper Confidence Bound Optimizing Ad Selection in Python.srt 12.7 kB06 - Simple Linear Regression/009 Step 4b - Evaluating Linear Regression Model Performance on Test Data.srt 12.6 kB33 - Thompson Sampling/006 Step 4 - Beating UCB with Thompson Sampling Python Multi-Armed Bandit Tutorial.srt 12.6 kB37 - Convolutional Neural Networks/014 Step 4 CNN Training - Epochs, Loss Function --& Metrics in TensorFlow.srt 12.6 kB07 - Multiple Linear Regression/004 How to Handle Categorical Variables in Linear Regression Models.srt 12.4 kB16 - Logistic Regression/005 Step 2a Python Data Preprocessing for Logistic Regression Dataset Prep.srt 12.2 kB16 - Logistic Regression/011 Step 5 - Comparing Predicted vs Real Results Python Logistic Regression Guide.srt 12.2 kB26 - K-Means Clustering/015 Step 5c - Analyzing Customer Segments Insights from K-means Clustering.srt 11.8 kB11 - Random Forest Regression/001 Understanding Random Forest Algorithm Intuition and Application in ML.srt 11.8 kB36 - Artificial Neural Networks/016 Step 2 - How to Install and Initialize H2O for Efficient Deep Learning in R.srt 11.7 kB03 - Data Preprocessing in Python/002 Step 2 - Data Preprocessing Techniques From Raw Data to ML-Ready Datasets.srt 11.7 kB34 - -------------------- Part 7 Natural Language Processing --------------------/017 Step 3 - NLP in R Initialising a Corpus for Sentiment Analysis.srt 11.6 kB09 - Support Vector Regression (SVR)/005 Step 2a - Mastering Feature Scaling for Support Vector Regression in Python.srt 11.4 kB37 - Convolutional Neural Networks/004 Step 1b - Applying ReLU to Convolutional Layers Breaking Up Image Linearity.srt 11.2 kB22 - Random Forest Classification/003 Step 2 Random Forest Evaluation - Confusion Matrix --& Accuracy Metrics.srt 11.0 kB02 - -------------------- Part 1 Data Preprocessing --------------------/004 Feature Scaling in Machine Learning Normalization vs Standardization Explained.srt 11.0 kB23 - Classification Model Selection in Python/004 Step 2 - Optimizing Model Selection Streamlined Classification Code in Python.srt 11.0 kB19 - Kernel SVM/007 Step 2 - Mastering Kernel SVM Improving Accuracy with Non-Linear Classifiers.srt 11.0 kB33 - Thompson Sampling/003 Step 1 - Python Implementation of Thompson Sampling for Bandit Problems.srt 10.9 kB20 - Naive Bayes/003 Bayes Theorem in Machine Learning Step-by-Step Probability Calculation.srt 10.9 kB08 - Polynomial Regression/003 Step 1b - Setting Up Data for Linear vs Polynomial Regression Comparison.srt 10.9 kB23 - Classification Model Selection in Python/005 Step 3 - Evaluating Classification Algorithms Accuracy Metrics in Python.srt 10.9 kB01 - Welcome to the course! Here we will help you get started in the best conditions/004 How to Use Google Colab --& Machine Learning Course Folder.srt 10.9 kB22 - Random Forest Classification/004 Step 1 Random Forest Classifier - From Template to Implementation in R.srt 10.8 kB03 - Data Preprocessing in Python/008 Coding Exercise 1 Importing and Preprocessing a Dataset for Machine Learning.html 10.8 kB11 - Random Forest Regression/005 Step 2 - Visualizing Random Forest Regression Interpreting Stairs and Splits.srt 10.7 kB07 - Multiple Linear Regression/012 Step 3a - Scikit-learn for Multiple Linear Regression Efficient Model Building.srt 10.7 kB26 - K-Means Clustering/016 Step 1 - K-Means Clustering in R Importing --& Exploring Segmentation Data.srt 10.7 kB21 - Decision Tree Classification/003 Step 2 - Training a Decision Tree Classifier Optimizing Performance in Python.srt 10.7 kB06 - Simple Linear Regression/004 Step 1b Data Preprocessing for Linear Regression Import --& Split Data in Python.srt 10.7 kB27 - Hierarchical Clustering/007 Step 2c - Interpreting Dendrograms Optimal Clusters in Hierarchical Clustering.srt 10.7 kB22 - Random Forest Classification/005 Step 2 Random Forest Classification - Visualizing Predictions --& Results.srt 10.7 kB21 - Decision Tree Classification/002 Step 1 - Implementing Decision Tree Classification in Python with Scikit-learn.srt 10.6 kB03 - Data Preprocessing in Python/019 Coding Exercise 4 Dataset Splitting and Feature Scaling.html 10.6 kB13 - Regression Model Selection in Python/003 Step 2 - Creating Generic Code Templates for Various Regression Models in Python.srt 10.6 kB11 - Random Forest Regression/002 Step 1 - Building a Random Forest Regression Model with Python and Scikit-Learn.srt 10.5 kB07 - Multiple Linear Regression/008 Step 1a - Hands-On Data Preprocessing for Multiple Linear Regression in Python.srt 10.5 kB17 - K-Nearest Neighbors (K-NN)/004 Step 3 - Visualizing KNN Decision Boundaries Python Tutorial for Beginners.srt 10.5 kB10 - Decision Tree Regression/008 Step 2 - Decision Tree Regression Fixing Splits with rpart Control Parameter.srt 10.4 kB18 - Support Vector Machine (SVM)/002 Step 1 - Building a Support Vector Machine Model with Scikit-learn in Python.srt 10.4 kB22 - Random Forest Classification/002 Step 1 - Implementing Random Forest Classification in Python with Scikit-Learn.srt 10.3 kB16 - Logistic Regression/006 Step 2b - Data Preprocessing Feature Scaling Techniques for Logistic Regression.srt 10.3 kB26 - K-Means Clustering/017 Step 2 - K-Means Algorithm Implementation in R Fitting and Analyzing Mall Data.srt 10.3 kB08 - Polynomial Regression/019 Step 1 - Building a Reusable Framework for Nonlinear Regression Analysis in R.srt 10.3 kB03 - Data Preprocessing in Python/020 Step 1 - Feature Scaling in ML Why It--'s Crucial for Data Preprocessing.srt 10.3 kB03 - Data Preprocessing in Python/013 Step 2 - Handling Categorical Data One-Hot Encoding with ColumnTransformer.srt 10.3 kB24 - Evaluating Classification Models Performance/004 Mastering CAP Analysis Assessing Classification Models with Accuracy Ratio.srt 10.3 kB09 - Support Vector Regression (SVR)/012 Step 1 - SVR Tutorial Creating a Support Vector Machine Regressor in R.srt 10.3 kB17 - K-Nearest Neighbors (K-NN)/003 Step 2 - Building a K-Nearest Neighbors Model Scikit-Learn KNeighborsClassifier.srt 10.3 kB23 - Classification Model Selection in Python/003 Step 1 - How to Choose the Right Classification Algorithm for Your Dataset.srt 10.3 kB03 - Data Preprocessing in Python/023 Step 4 - Applying the Same Scaler to Training and Test Sets in Python.srt 10.3 kB08 - Polynomial Regression/004 Step 2a Linear to Polynomial Regression - Preparing Data for Advanced Models.srt 10.3 kB03 - Data Preprocessing in Python/006 Step 3 - Preprocessing Data Building X and Y Vectors for ML Model Training.srt 10.3 kB18 - Support Vector Machine (SVM)/003 Step 2 - Building a Support Vector Machine Model with Sklearn--'s SVC in Python.srt 10.2 kB08 - Polynomial Regression/005 Step 2b - Transforming Linear to Polynomial Regression A Step-by-Step Guide.srt 10.2 kB08 - Polynomial Regression/006 Step 3a - Plotting Real vs Predicted Salaries Linear Regression Visualization.srt 10.2 kB19 - Kernel SVM/006 Step 1 - Python Kernel SVM Applying RBF to Solve Non-Linear Classification.srt 10.2 kB01 - Welcome to the course! Here we will help you get started in the best conditions/005 Getting Started with R Programming Install R and RStudio on Windows --& Mac.srt 10.2 kB11 - Random Forest Regression/004 Step 1 - Building a Random Forest Model in R Regression Tutorial.srt 10.2 kB21 - Decision Tree Classification/004 Step 1 - R Tutorial Creating a Decision Tree Classifier with rpart Library.srt 10.2 kB20 - Naive Bayes/005 Step 1 - Naive Bayes in Python Applying ML to Social Network Ads Optimisation.srt 10.2 kB27 - Hierarchical Clustering/004 Step 1 - Getting Started with Hierarchical Clustering Data Setup in Python.srt 10.2 kB16 - Logistic Regression/018 Step 1 - Data Preprocessing for Logistic Regression in R Preparing Your Dataset.srt 10.2 kB16 - Logistic Regression/012 Step 6a - Implementing Confusion Matrix and Accuracy Score in Scikit-Learn.srt 10.1 kB06 - Simple Linear Regression/008 Step 4a - Linear Regression Plotting Real vs Predicted Salaries Visualization.srt 10.1 kB03 - Data Preprocessing in Python/017 Step 2 - Preparing Data Creating Training and Test Sets in Python for ML Models.srt 10.1 kB20 - Naive Bayes/006 Step 2 - Python Naive Bayes Training and Evaluating a Classifier on Real Data.srt 10.1 kB16 - Logistic Regression/024 Step 5b Logistic Regression - Linear Classifiers --& Prediction Boundaries.srt 10.1 kB06 - Simple Linear Regression/003 Step 1a - Mastering Simple Linear Regression Key Concepts and Implementation.srt 10.1 kB07 - Multiple Linear Regression/014 Step 4a Comparing Real vs Predicted Profits in Linear Regression - Hands-on Gui.srt 10.1 kB06 - Simple Linear Regression/012 Step 2 - Fitting Simple Linear Regression in R LM Function and Model Summary.srt 10.1 kB34 - -------------------- Part 7 Natural Language Processing --------------------/020 Step 6 - Cleaning Text Data Removing Punctuation for NLP and Classification.srt 10.0 kB17 - K-Nearest Neighbors (K-NN)/002 Step 1 - Python KNN Tutorial Classifying Customer Data for Targeted Marketing.srt 10.0 kB03 - Data Preprocessing in Python/009 Step 1 - Using Scikit-Learn to Replace Missing Values in Machine Learning.srt 10.0 kB11 - Random Forest Regression/003 Step 2 - Creating a Random Forest Regressor Key Parameters and Model Fitting.srt 10.0 kB09 - Support Vector Regression (SVR)/008 Step 3 SVM Regression Creating --& Training SVR Model with RBF Kernel in Python.srt 10.0 kB21 - Decision Tree Classification/005 Step 2 - Decision Tree Classifier Optimizing Prediction Boundaries in R.srt 10.0 kB26 - K-Means Clustering/013 Step 5a Visualizing K-Means Clusters of Customer Data with Python Scatter.srt 10.0 kB19 - Kernel SVM/008 Step 1 - Kernel SVM vs Linear SVM Overcoming Non-Linear Separability in R.srt 9.9 kB04 - Data Preprocessing in R/005 Using R--'s Factor Function to Handle Categorical Variables in Data Analysis.srt 9.9 kB26 - K-Means Clustering/010 Step 3b - Optimizing K-means Clustering WCSS and Elbow Method Implementation.srt 9.9 kB27 - Hierarchical Clustering/006 Step 2b - Visualizing Hierarchical Clustering Dendrogram Basics in Python.srt 9.8 kB04 - Data Preprocessing in R/010 Essential Steps in Data Preprocessing Preparing Your Dataset for ML Models.srt 9.8 kB04 - Data Preprocessing in R/004 How to Handle Missing Values in R Data Preprocessing for Machine Learning.srt 9.8 kB09 - Support Vector Regression (SVR)/003 Step 1a - SVR Model Training Feature Scaling and Dataset Preparation in Python.srt 9.8 kB08 - Polynomial Regression/016 Step 3c - Polynomial Regression Curve Fitting for Better Predictions.srt 9.8 kB26 - K-Means Clustering/012 Step 4 - Creating a Dependent Variable from K-Means Clustering Results in Python.srt 9.8 kB08 - Polynomial Regression/007 Step 3b - Polynomial vs Linear Regression Better Fit with Higher Degrees.srt 9.8 kB16 - Logistic Regression/023 Step 5a - Interpreting Logistic Regression Plots Prediction Regions Explained.srt 9.8 kB22 - Random Forest Classification/006 Step 3 - Evaluating Random Forest Performance Test Set Results --& Overfitting.srt 9.7 kB06 - Simple Linear Regression/014 Step 4a - Plotting Linear Regression Data in R ggplot2 Step-by-Step Guide.srt 9.7 kB03 - Data Preprocessing in Python/010 Step 2 - Imputing Missing Data in Python SimpleImputer and Numerical Columns.srt 9.7 kB39 - Principal Component Analysis (PCA)/003 Step 2 - PCA in Action Reducing Dimensions and Predicting Customer Segments.srt 9.7 kB27 - Hierarchical Clustering/009 Step 3b - Comparing 3 vs 5 Clusters in Hierarchical Clustering Python Example.srt 9.7 kB30 - Eclat/001 Mastering ECLAT Support-Based Approach to Market Basket Optimization.srt 9.6 kB16 - Logistic Regression/009 Step 4a - Formatting Single Observation Input for Logistic Regression Predict.srt 9.5 kB19 - Kernel SVM/009 Step 2 - Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning.srt 9.5 kB11 - Random Forest Regression/006 Step 3 - Fine-Tuning Random Forest From 10 to 500 Trees for Accurate Prediction.srt 9.5 kB26 - K-Means Clustering/009 Step 3a - Implementing the Elbow Method for K-Means Clustering in Python.srt 9.5 kB27 - Hierarchical Clustering/008 Step 3a - Building a Hierarchical Clustering Model with Scikit-learn in Python.srt 9.5 kB16 - Logistic Regression/003 Step 1a - Building a Logistic Regression Model for Customer Behavior Prediction.srt 9.5 kB17 - K-Nearest Neighbors (K-NN)/005 Step 1 - Implementing KNN Classification in R Setup --& Data Preparation.srt 9.5 kB16 - Logistic Regression/027 Optimizing R Scripts for Machine Learning Building a Classification Template.srt 9.5 kB18 - Support Vector Machine (SVM)/006 Step 2 Creating --& Evaluating Linear SVM Classifier in R - Predictions --& Results.srt 9.5 kB34 - -------------------- Part 7 Natural Language Processing --------------------/022 Step 8 - Enhancing Text Classification Stemming for Efficient Feature Matrices.srt 9.4 kB08 - Polynomial Regression/020 Step 2 - Mastering Regression Model Visualization Increasing Data Resolution.srt 9.4 kB07 - Multiple Linear Regression/015 Step 4b - ML in Python Evaluating Multiple Linear Regression Accuracy.srt 9.4 kB16 - Logistic Regression/014 Step 7a - Visualizing Logistic Regression Decision Boundaries in Python 2D Plot.srt 9.4 kB17 - K-Nearest Neighbors (K-NN)/001 K-Nearest Neighbors --(KNN--) Explained A Beginner--'s Guide to Classification.srt 9.4 kB27 - Hierarchical Clustering/011 Step 2 Using H.clust in R - Building --& Interpreting Dendrograms for Clustering.srt 9.4 kB26 - K-Means Clustering/008 Step 2b K-Means Clustering - Optimizing Features for 2D Visualization.srt 9.3 kB08 - Polynomial Regression/015 Step 3b Visualizing Linear Regression - Plotting Predictions vs Observations.srt 9.2 kB18 - Support Vector Machine (SVM)/005 Step 1 - Building a Linear SVM Classifier in R Data Import and Initial Setup.srt 9.2 kB03 - Data Preprocessing in Python/001 Step 1 - Data Preprocessing in Python Preparing Your Dataset for ML Models.srt 9.2 kB07 - Multiple Linear Regression/020 Step 2a - Multiple Linear Regression in R Building --& Interpreting the Regressor.srt 9.1 kB36 - Artificial Neural Networks/009 Bank Customer Churn Prediction Machine Learning Model with TensorFlow.srt 9.1 kB03 - Data Preprocessing in Python/004 Step 1 - Machine Learning Basics Importing Datasets Using Pandas read_csv--(--).srt 9.0 kB19 - Kernel SVM/010 Step 3 Visualizing Kernel SVM - Non-Linear Classification in Machine Learning.srt 9.0 kB21 - Decision Tree Classification/006 Step 3 - Decision Tree Visualization Exploring Splits and Conditions in R.srt 9.0 kB08 - Polynomial Regression/001 Understanding Polynomial Linear Regression Applications and Examples.srt 8.9 kB10 - Decision Tree Regression/007 Step 1 - Creating a Decision Tree Regressor Using rpart Function in R.srt 8.9 kB10 - Decision Tree Regression/009 Step 3 Non-Continuous Regression - Decision Tree Visualization Challenges.srt 8.9 kB04 - Data Preprocessing in R/007 Step 2 - Preparing Data Creating Training and Test Sets in R for ML Models.srt 8.9 kB10 - Decision Tree Regression/004 Step 2 - Implementing DecisionTreeRegressor A Step-by-Step Guide in Python.srt 8.8 kB09 - Support Vector Regression (SVR)/013 Step 2 - Support Vector Regression Building a Predictive Model in Python.srt 8.8 kB36 - Artificial Neural Networks/008 Deep Learning Fundamentals Training Neural Networks Step-by-Step.srt 8.8 kB10 - Decision Tree Regression/006 Step 4 - Visualizing Decision Tree Regression High-Resolution Results.srt 8.7 kB06 - Simple Linear Regression/015 Step 4b - Creating a Scatter Plot with Regression Line in R Using ggplot2.srt 8.7 kB12 - Evaluating Regression Models Performance/002 Understanding Adjusted R-Squared Key Differences from R-Squared Explained.srt 8.6 kB07 - Multiple Linear Regression/011 Step 2b - Multiple Linear Regression in Python Preparing Your Dataset.srt 8.6 kB16 - Logistic Regression/020 Step 3 - How to Use R for Logistic Regression Prediction Step-by-Step Guide.srt 8.6 kB43 - Model Selection/002 How to Master the Bias-Variance Tradeoff in Machine Learning Models.srt 8.6 kB01 - Welcome to the course! Here we will help you get started in the best conditions/002 Get Excited about ML Predict Car Purchases with Python --& Scikit-learn in 5 mins.srt 8.5 kB06 - Simple Linear Regression/011 Step 1 - Data Preprocessing in R Preparing for Linear Regression Modeling.srt 8.5 kB13 - Regression Model Selection in Python/006 Step 1 - Selecting the Best Regression Model R-squared Evaluation in Python.srt 8.5 kB18 - Support Vector Machine (SVM)/005 SVM.zip 8.5 kB16 - Logistic Regression/001 Understanding Logistic Regression Predicting Categorical Outcomes.srt 8.4 kB26 - K-Means Clustering/005 Step 1a - Python K-Means Tutorial Identifying Customer Patterns in Mall Data.srt 8.4 kB17 - K-Nearest Neighbors (K-NN)/007 Step 3 - Implementing KNN Classification in R Adapting the Classifier Template.srt 8.4 kB16 - Logistic Regression/025 Step 5c - Data Viz in R Colorizing Pixels for Logistic Regression.srt 8.4 kB26 - K-Means Clustering/014 Step 5b - Visualizing K-Means Clusters Plotting Customer Segments in Python.srt 8.3 kB06 - Simple Linear Regression/007 Step 3 - Using Scikit-Learn--'s Predict Method for Linear Regression in Python.srt 8.3 kB27 - Hierarchical Clustering/005 Step 2a - Implementing Hierarchical Clustering Building a Dendrogram with SciPy.srt 8.3 kB08 - Polynomial Regression/014 Step 3a Visualizing Regression Results - Creating Scatter Plots with ggplot2 in.srt 8.3 kB26 - K-Means Clustering/004 K-Means++ Algorithm Solving the Random Initialization Trap in Clustering.srt 8.3 kB08 - Polynomial Regression/013 Step 2b - Building a Polynomial Regression Model Adding Squared --& Cubed Terms.srt 8.2 kB22 - Random Forest Classification/001 Understanding Random Forest Decision Trees and Majority Voting Explained.srt 8.2 kB06 - Simple Linear Regression/016 Step 4c - Comparing Training vs Test Set Predictions in Linear Regression.srt 8.2 kB09 - Support Vector Regression (SVR)/006 Step 2b Reshaping Data for SVR - Preparing Y Vector for Feature Scaling --(Python.srt 8.2 kB04 - Data Preprocessing in R/006 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.srt 8.2 kB03 - Data Preprocessing in Python/005 Step 2 - Using Pandas iloc for Feature Selection in ML Data Preprocessing.srt 8.2 kB10 - Decision Tree Regression/002 Step 1a - Decision Tree Regression Building a Model without Feature Scaling.srt 8.2 kB04 - Data Preprocessing in R/009 How to Scale Numeric Features in R for Machine Learning Preprocessing - Step 2.srt 8.2 kB08 - Polynomial Regression/012 Step 2a - Building Linear --& Polynomial Regression Models in R A Comparison.srt 8.1 kB07 - Multiple Linear Regression/013 Step 3b - Scikit-Learn Building --& Training Multiple Linear Regression Models.srt 8.1 kB26 - K-Means Clustering/007 Step 2a - K-Means Clustering in Python Selecting Relevant Features for Analysis.srt 8.1 kB03 - Data Preprocessing in Python/021 Step 2 - How to Scale Numeric Features in Python for ML Preprocessing.srt 8.1 kB20 - Naive Bayes/008 Step 1 - Getting Started with Naive Bayes Algorithm in R for Classification.srt 8.0 kB07 - Multiple Linear Regression/003 Understanding Linear Regression Assumptions Linearity, Homoscedasticity --& More.srt 7.9 kB17 - K-Nearest Neighbors (K-NN)/006 Step 2 - Building a KNN Classifier Preparing Training and Test Sets in R.srt 7.9 kB20 - Naive Bayes/009 Step 2 - Troubleshooting Naive Bayes Classification Empty Prediction Vectors.srt 7.9 kB23 - Classification Model Selection in Python/002 Mastering the Confusion Matrix True Positives, Negatives, and Errors.srt 7.9 kB03 - Data Preprocessing in Python/014 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt 7.9 kB07 - Multiple Linear Regression/010 Step 2a - Hands-on Multiple Linear Regression Preparing Data in Python.srt 7.9 kB13 - Regression Model Selection in Python/002 Step 1 - Mastering Regression Toolkit Comparing Models for Optimal Performance.srt 7.9 kB01 - Welcome to the course! Here we will help you get started in the best conditions/001 Welcome Challenge!.html 7.8 kB12 - Evaluating Regression Models Performance/001 Understanding R-squared Evaluating Goodness of Fit in Regression Models.srt 7.7 kB08 - Polynomial Regression/002 Step 1a - Building a Polynomial Regression Model for Salary Prediction in Python.srt 7.5 kB13 - Regression Model Selection in Python/004 Step 3 Evaluating Regression Models - R-Squared --& Performance Metrics Explained.srt 7.4 kB07 - Multiple Linear Regression/022 Step 3 - How to Use predict--(--) Function in R for Multiple Linear Regression.srt 7.4 kB06 - Simple Linear Regression/006 Step 2b - Machine Learning Basics Training a Linear Regression Model in Python.srt 7.3 kB04 - Data Preprocessing in R/008 Feature Scaling in ML Step 1 Why It--'s Crucial for Data Preprocessing.srt 7.3 kB16 - Logistic Regression/004 Step 1b - Implementing Logistic Regression in Python Data Preprocessing Guide.srt 7.3 kB13 - Regression Model Selection in Python/007 Step 2 - Selecting the Best Regression Model Random Forest vs. SVR Performance.srt 7.2 kB10 - Decision Tree Regression/003 Step 1b Uploading --& Preprocessing Data for Decision Tree Regression in Python.srt 7.2 kB03 - Data Preprocessing in Python/012 Step 1 - One-Hot Encoding Transforming Categorical Features for ML Algorithms.srt 7.1 kB46 - Congratulations!! 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Logistic Regression/007 Step 3a - How to Import and Use LogisticRegression Class from Scikit-learn.srt 6.8 kB32 - Upper Confidence Bound (UCB)/004 Step 2 Implementing UCB Algorithm in Python - Data Preparation.srt 6.7 kB06 - Simple Linear Regression/005 Step 2a - Building a Simple Linear Regression Model with Scikit-learn in Python.srt 6.7 kB10 - Decision Tree Regression/010 Step 4 - Visualizing Decision Tree Understanding Intervals and Predictions.srt 6.7 kB08 - Polynomial Regression/009 Step 4b Python Polynomial Regression - Predicting Salaries Accurately.srt 6.6 kB07 - Multiple Linear Regression/019 Step 1b - Preparing Datasets for Multiple Linear Regression in R.srt 6.6 kB08 - Polynomial Regression/008 Step 4a Predicting Salaries - Linear Regression in Python --(Array Input Guide--).srt 6.6 kB08 - Polynomial Regression/010 Step 1a - Implementing Polynomial Regression in R HR Salary Analysis Case Study.srt 6.6 kB07 - Multiple Linear Regression/018 Step 1a - Data Preprocessing for MLR Handling Categorical Data.srt 6.5 kB34 - -------------------- Part 7 Natural Language Processing --------------------/003 Deep NLP --& Sequence-to-Sequence Models Exploring Natural Language Processing.srt 6.5 kB26 - K-Means Clustering/011 Step 3c - Plotting the Elbow Method Graph for K-Means Clustering in Python.srt 6.5 kB09 - Support Vector Regression (SVR)/009 Step 4 - SVR Model Prediction Handling Scaled Data and Inverse Transformation.srt 6.5 kB09 - Support Vector Regression (SVR)/010 Step 5a - How to Plot Support Vector Regression --(SVR--) Models Step-by-Step Guide.srt 6.5 kB03 - Data Preprocessing in Python/016 Step 1 - How to Prepare Data for Machine Learning Training vs Test Sets.srt 6.5 kB09 - Support Vector Regression (SVR)/011 Step 5b - SVR Scaling --& Inverse Transformation in Python.srt 6.5 kB03 - Data Preprocessing in Python/022 Step 3 - Implementing Feature Scaling Fit and Transform Methods Explained.srt 6.4 kB07 - Multiple Linear Regression/001 Startup Success Prediction Regression Model for VC Fund Decision-Making.srt 6.4 kB03 - 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Simple Linear Regression/013 Step 3 - How to Use predict--(--) Function in R for Linear Regression Analysis.srt 6.0 kB26 - K-Means Clustering/001 What is Clustering in Machine Learning Introduction to Unsupervised Learning.srt 6.0 kB34 - -------------------- Part 7 Natural Language Processing --------------------/021 Step 7 - Simplifying Corpus Using SnowballC Package to Remove Stop Words in R.srt 6.0 kB07 - Multiple Linear Regression/016 Multiple Linear Regression in Python - Backward Elimination.html 5.9 kB39 - Principal Component Analysis (PCA)/001 PCA Algorithm Intuition Reducing Dimensions in Unsupervised Learning.srt 5.8 kB16 - Logistic Regression/013 Step 6b Evaluating Classification Models - Confusion Matrix --& Accuracy Metrics.srt 5.8 kB33 - Thompson Sampling/009 Step 2 - Reinforcement Learning Thompson Sampling Outperforms UCB Algorithm.srt 5.7 kB24 - Evaluating Classification Models Performance/005 Conclusion of Part 3 - Classification.html 5.7 kB16 - Logistic Regression/008 Step 3b - Training Logistic Regression Model Fit Method for Classification.srt 5.7 kB09 - Support Vector Regression (SVR)/007 Step 2c SVR Data Prep - Scaling X --& Y Independently with StandardScaler.srt 5.7 kB16 - Logistic Regression/016 Step 7c - Visualizing Logistic Regression Performance on New Data in Python.srt 5.6 kB10 - Decision Tree Regression/005 Step 3 - Implementing Decision Tree Regression in Python Making Predictions.srt 5.5 kB27 - Hierarchical Clustering/012 Step 3 - Implementing Hierarchical Clustering Using Cat Tree Method in R.srt 5.4 kB06 - Simple Linear Regression/002 How to Find the Best Fit Line Understanding Ordinary Least Squares Regression.srt 5.4 kB19 - Kernel SVM/001 From Linear to Non-Linear SVM Exploring Higher Dimensional Spaces.srt 5.3 kB34 - -------------------- Part 7 Natural Language Processing --------------------/018 Step 4 - NLP Data Cleaning Lowercase Transformation in R for Text Analysis.srt 5.3 kB32 - Upper Confidence Bound (UCB)/013 Step 4 - UCB Algorithm Performance Analyzing Ad Selection with Histograms.srt 5.2 kB16 - Logistic Regression/019 Step 2 - How to Create a Logistic Regression Classifier Using R--'s GLM Function.srt 5.1 kB23 - Classification Model Selection in Python/006 Step 4 - Model Selection Process Evaluating Classification Algorithms.srt 5.1 kB34 - -------------------- Part 7 Natural Language Processing --------------------/002 NLP Basics Understanding Bag of Words and Its Applications in Machine Learning.srt 5.1 kB26 - K-Means Clustering/006 Step 1b K-Means Clustering - Data Preparation in Google ColabJupyter.srt 5.1 kB26 - K-Means Clustering/002 K-Means Clustering Tutorial Visualizing the Machine Learning Algorithm.srt 4.9 kB18 - Support Vector Machine (SVM)/004 Step 3 - Understanding Linear SVM Limitations Why It Didn--'t Beat kNN Classifier.srt 4.9 kB04 - Data Preprocessing in R/003 R Tutorial Importing and Viewing Datasets for Data Preprocessing.srt 4.8 kB36 - Artificial Neural Networks/001 Understanding CNN Layers Convolution, ReLU, Pooling, and Flattening Explained.srt 4.6 kB07 - Multiple Linear Regression/009 Step 1b - Hands-On Guide Implementing Multiple Linear Regression in Python.srt 4.6 kB33 - Thompson Sampling/007 Additional Resource for this Section.html 4.6 kB27 - Hierarchical Clustering/013 Step 4 - Cluster Plot Method Visualizing Hierarchical Clustering Results in R.srt 4.5 kB27 - Hierarchical Clustering/014 Step 5 - Hierarchical Clustering in R Understanding Customer Spending Patterns.srt 4.5 kB16 - Logistic Regression/021 Step 4 - How to Assess Model Accuracy Using a Confusion Matrix in R.srt 4.4 kB15 - -------------------- Part 3 Classification --------------------/002 What is Classification in Machine Learning Fundamentals and Applications.srt 4.2 kB07 - Multiple Linear Regression/002 Multiple Linear Regression Independent Variables --& Prediction Models.srt 4.2 kB16 - Logistic Regression/022 Warning - Update.html 4.1 kB46 - Congratulations!! Don't forget your Prize )/002 Bonus How To UNLOCK Top Salaries (Live Training).html 4.1 kB34 - -------------------- Part 7 Natural Language Processing --------------------/001 Welcome to Part 7 - Natural Language Processing.html 4.1 kB13 - Regression Model Selection in Python/008 Conclusion of Part 2 - Regression.html 4.0 kB14 - Regression Model Selection in R/003 Conclusion of Part 2 - Regression.html 4.0 kB03 - Data Preprocessing in Python/007 For Python learners, summary of Object-oriented programming classes & objects.html 3.9 kB31 - -------------------- Part 6 Reinforcement Learning --------------------/001 Welcome to Part 6 - Reinforcement Learning.html 3.8 kB07 - Multiple Linear Regression/005 Multicollinearity in Regression Understanding the Dummy Variable Trap.srt 3.8 kB19 - Kernel SVM/004 Understanding Different Types of Kernel Functions for Machine Learning.srt 3.8 kB34 - -------------------- Part 7 Natural Language Processing --------------------/025 Homework Challenge.html 3.7 kB34 - -------------------- Part 7 Natural Language Processing --------------------/019 Step 5 - Sentiment Analysis Data Cleaning Removing Numbers with TM Map.srt 3.7 kB06 - Simple Linear Regression/001 Simple Linear Regression Understanding the Equation and Potato Yield Prediction.srt 3.7 kB34 - -------------------- Part 7 Natural Language Processing --------------------/013 Homework Challenge.html 3.6 kB24 - Evaluating Classification Models Performance/002 Machine Learning Model Evaluation Accuracy Paradox and Better Metrics.srt 3.6 kB07 - Multiple Linear Regression/017 Multiple Linear Regression in Python - EXTRA CONTENT.html 3.5 kB38 - -------------------- Part 9 Dimensionality Reduction --------------------/001 Welcome to Part 9 - Dimensionality Reduction.html 3.5 kB06 - Simple Linear Regression/010 Simple Linear Regression in Python - Additional Lecture.html 3.5 kB44 - XGBoost/002 Model Selection and Boosting Additional Content.html 3.5 kB34 - -------------------- Part 7 Natural Language Processing --------------------/012 Natural Language Processing in Python - EXTRA.html 3.4 kB04 - Data Preprocessing in R/002 Data Preprocessing Tutorial Understanding Independent vs Dependent Variables.srt 3.4 kB02 - -------------------- Part 1 Data Preprocessing --------------------/003 Data Preprocessing Importance of Training-Test Split in ML Model Evaluation.srt 3.4 kB01 - Welcome to the course! Here we will help you get started in the best conditions/006 EXTRA Use ChatGPT to Boost your ML Skills.html 3.3 kB37 - Convolutional Neural Networks/006 Step 3 - Understanding Flattening in Convolutional Neural Network Architecture.srt 3.3 kB23 - Classification Model Selection in Python/001 Make sure you have this Model Selection folder ready.html 3.3 kB13 - Regression Model Selection in Python/001 Make sure you have this Model Selection folder ready.html 3.3 kB36 - Artificial Neural Networks/019 Deep Learning Additional Content.html 3.3 kB42 - -------------------- Part 10 Model Selection & Boosting --------------------/001 Welcome to Part 10 - Model Selection & Boosting.html 3.2 kB37 - Convolutional Neural Networks/017 Deep Learning Additional Content #2.html 3.2 kB35 - -------------------- Part 8 Deep Learning --------------------/001 Welcome to Part 8 - Deep Learning.html 3.2 kB15 - -------------------- Part 3 Classification --------------------/001 Welcome to Part 3 - Classification.html 3.1 kB16 - Logistic Regression/010 Step 4b Predicted vs. Real Purchase Decisions in Logistic Regression.srt 3.1 kB16 - Logistic Regression/028 Machine Learning Regression and Classification EXTRA.html 3.1 kB05 - -------------------- Part 2 Regression --------------------/001 Welcome to Part 2 - Regression.html 3.1 kB07 - Multiple Linear Regression/025 Multiple Linear Regression in R - Automatic Backward Elimination.html 3.1 kB37 - Convolutional Neural Networks/010 Make sure you have your dataset ready.html 3.1 kB25 - -------------------- Part 4 Clustering --------------------/001 Welcome to Part 4 - Clustering.html 3.0 kB16 - Logistic Regression/017 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html 3.0 kB16 - Logistic Regression/026 Logistic Regression in R - Step 5 (Colour-blind friendly image).html 3.0 kB20 - Naive Bayes/007 Step 3 - Analyzing Naive Bayes Algorithm Results Accuracy and Predictions.srt 3.0 kB34 - -------------------- Part 7 Natural Language Processing --------------------/015 Warning - Update.html 3.0 kB16 - Logistic Regression/030 EXTRA CONTENT Logistic Regression Practical Case Study.html 2.9 kB04 - Data Preprocessing in R/001 Data Preprocessing for Beginners Preparing Your Dataset for Machine Learning.srt 2.8 kB02 - -------------------- Part 1 Data Preprocessing --------------------/002 Machine Learning Workflow Importing, Modeling, and Evaluating Your ML Model.srt 2.8 kB02 - -------------------- Part 1 Data Preprocessing --------------------/001 Welcome to Part 1 - Data Preprocessing.html 2.8 kB36 - Artificial Neural Networks/020 EXTRA CONTENT ANN Case Study.html 2.8 kB28 - -------------------- Part 5 Association Rule Learning --------------------/001 Welcome to Part 5 - Association Rule Learning.html 2.8 kB01 - Welcome to the course! Here we will help you get started in the best conditions/003 Get all the Datasets, Codes and Slides here.html 2.7 kB27 - Hierarchical Clustering/016 Conclusion of Part 4 - Clustering.html 2.7 kB
07 - Multiple Linear Regression/external-links.txt 70 Bytes
07 - Multiple Linear Regression/003 Download-the-PDF.url 68 Bytes
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