magnet:?xt=urn:btih:7171D3C9B64AF182F6C5C1F4B57CEE8DAA45808C
11 - Probability Bayesian Inference/51 - A Practical Example of Bayesian Inference.mp4 313.5 MB12 - Probability Distributions/66 - A Practical Example of Probability Distributions.mp4 297.5 MB16 - Statistics Practical Example Descriptive Statistics/93 - Practical Example Descriptive Statistics.mp4 259.3 MB35 - Advanced Statistical Methods Practical Example Linear Regression/224 - Practical Example Linear Regression Part 1.mp4 184.7 MB5 - The Field of Data Science Popular Data Science Techniques/11 - Techniques for Working with Traditional Data.mp4 173.6 MB64 - Appendix Working with Text Files in Python/505 - Importing Data from json Files.mp4 167.5 MB58 - Case Study Preprocessing the Absenteeismdata/420 - Obtaining Dummies from a Single Feature.mp4 159.1 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - Business Case Preprocessing.mp4 153.4 MB51 - Deep Learning Business Case Example/354 - Business Case Preprocessing the Data.mp4 152.2 MB3 - The Field of Data Science Connecting the Data Science Disciplines/9 - Applying Traditional Data Big Data BI Traditional Data Science and ML.mp4 141.2 MB19 - Statistics Practical Example Inferential Statistics/118 - Practical Example Inferential Statistics.mp4 140.5 MB6 - The Field of Data Science Popular Data Science Tools/22 - Necessary Programming Languages and Software Used in Data Science.mp4 138.5 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Business Case Getting Acquainted with the Dataset.mp4 130.7 MB58 - Case Study Preprocessing the Absenteeismdata/425 - Classifying the Various Reasons for Absence.mp4 128.6 MB10 - Probability Combinatorics/39 - A Practical Example of Combinatorics.mp4 126.7 MB58 - Case Study Preprocessing the Absenteeismdata/412 - Checking the Content of the Data Set.mp4 121.7 MB40 - Part 6 Mathematics/281 - Why is Linear Algebra Useful.mp4 118.9 MB5 - The Field of Data Science Popular Data Science Techniques/17 - Techniques for Working with Traditional Methods.mp4 118.2 MB64 - Appendix Working with Text Files in Python/502 - Importing Data with loadtxt and genfromtxt.mp4 116.3 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/395 - Creating a Data Provider.mp4 115.6 MB5 - The Field of Data Science Popular Data Science Techniques/20 - Types of Machine Learning.mp4 114.1 MB64 - Appendix Working with Text Files in Python/498 - Importing csv Files Part I.mp4 109.4 MB35 - Advanced Statistical Methods Practical Example Linear Regression/231 - Practical Example Linear Regression Part 5.mp4 108.0 MB51 - Deep Learning Business Case Example/351 - Business Case Exploring the Dataset and Identifying Predictors.mp4 106.0 MB18 - Statistics Inferential Statistics Confidence Intervals/104 - Confidence Intervals Population Variance Known Zscore.mp4 105.8 MB5 - The Field of Data Science Popular Data Science Techniques/13 - Techniques for Working with Big Data.mp4 105.8 MB60 - Case Study Loading the absenteeismmodule/461 - Deploying the absenteeismmodule Part II.mp4 105.6 MB56 - Software Integration/404 - Taking a Closer Look at APIs.mp4 102.2 MB8 - The Field of Data Science Debunking Common Misconceptions/24 - Debunking Common Misconceptions.mp4 100.9 MB18 - Statistics Inferential Statistics Confidence Intervals/111 - Confidence intervals Two means Dependent samples.mp4 96.7 MB4 - The Field of Data Science The Benefits of Each Discipline/10 - The Reason Behind These Disciplines.mp4 95.3 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - Business Case Model Outline.mp4 93.8 MB64 - Appendix Working with Text Files in Python/500 - Importing csv Files Part III.mp4 93.0 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - MNIST Results and Testing.mp4 92.8 MB64 - Appendix Working with Text Files in Python/506 - An Introduction to Working with Excel Files in Python.mp4 92.4 MB64 - Appendix Working with Text Files in Python/508 - Importing Data in Python an Important Exercise.mp4 92.0 MB56 - Software Integration/403 - What are Data Connectivity APIs and Endpoints.mp4 91.4 MB64 - Appendix Working with Text Files in Python/503 - Importing Data Partial Cleaning While Importing Data.mp4 90.5 MB21 - Statistics Practical Example Hypothesis Testing/135 - Practical Example Hypothesis Testing.mp4 89.0 MB51 - Deep Learning Business Case Example/359 - Business Case Setting an Early Stopping Mechanism.mp4 89.0 MB58 - Case Study Preprocessing the Absenteeismdata/416 - Dropping a Column from a DataFrame in Python.mp4 84.6 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/450 - Interpreting the Coefficients for Our Problem.mp4 84.2 MB61 - Case Study Analyzing the Predicted Outputs in Tableau/466 - Analyzing Reasons vs Probability in Tableau.mp4 84.0 MB58 - Case Study Preprocessing the Absenteeismdata/436 - Extracting the Month Value from the Date Column.mp4 81.1 MB61 - Case Study Analyzing the Predicted Outputs in Tableau/464 - Analyzing Age vs Probability in Tableau.mp4 80.7 MB35 - Advanced Statistical Methods Practical Example Linear Regression/229 - Practical Example Linear Regression Part 4.mp4 79.2 MB2 - The Field of Data Science The Various Data Science Disciplines/8 - A Breakdown of our Data Science Infographic.mp4 78.0 MB63 - Appendix pandas Fundamentals/485 - Data Selection in pandas DataFrames.mp4 77.1 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/448 - Fitting the Model and Assessing its Accuracy.mp4 76.7 MB40 - Part 6 Mathematics/280 - Dot Product of Matrices.mp4 76.1 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/447 - Splitting the Data for Training and Testing.mp4 73.3 MB5 - The Field of Data Science Popular Data Science Techniques/15 - Business Intelligence BI Techniques.mp4 73.1 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dealing with Categorical Data Dummy Variables.mp4 72.6 MB34 - Advanced Statistical Methods Linear Regression with sklearn/223 - Train Test Split Explained.mp4 71.4 MB1 - Part 1 Introduction/1 - A Practical Example What You Will Learn in This Course.mp4 71.1 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Adjusted RSquared.mp4 70.4 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/451 - Standardizing only the Numerical Variables Creating a Custom Scaler.mp4 70.1 MB20 - Statistics Hypothesis Testing/129 - Test for the Mean Dependent Samples.mp4 69.5 MB9 - Part 2 Probability/26 - Computing Expected Values.mp4 69.1 MB5 - The Field of Data Science Popular Data Science Techniques/19 - Machine Learning ML Techniques.mp4 69.0 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - MNIST Model Outline.mp4 68.9 MB38 - Advanced Statistical Methods KMeans Clustering/254 - A Simple Example of Clustering.mp4 68.6 MB22 - Part 4 Introduction to Python/140 - Installing Python and Jupyter.mp4 67.8 MB15 - Statistics Descriptive Statistics/71 - Types of Data.mp4 67.7 MB62 - Appendix Additional Python Tools/472 - Triple Nested For Loops.mp4 67.1 MB38 - Advanced Statistical Methods KMeans Clustering/264 - Market Segmentation with Cluster Analysis Part 2.mp4 66.7 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/444 - Creating the Targets for the Logistic Regression.mp4 66.6 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Backward Elimination or How to Simplify Your Model.mp4 66.4 MB28 - Python Sequences/169 - Dictionaries.mp4 66.3 MB13 - Probability Probability in Other Fields/67 - Probability in Finance.mp4 65.5 MB56 - Software Integration/406 - Software Integration Explained.mp4 65.5 MB63 - Appendix pandas Fundamentals/486 - pandas DataFrames Indexing with iloc.mp4 65.1 MB35 - Advanced Statistical Methods Practical Example Linear Regression/225 - Practical Example Linear Regression Part 2.mp4 64.9 MB51 - Deep Learning Business Case Example/358 - Business Case Learning and Interpreting the Result.mp4 64.4 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/454 - Testing the Model We Created.mp4 64.2 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - MNIST Learning.mp4 62.9 MB34 - Advanced Statistical Methods Linear Regression with sklearn/207 - Simple Linear Regression with sklearn.mp4 62.6 MB20 - Statistics Hypothesis Testing/122 - Rejection Region and Significance Level.mp4 62.3 MB50 - Deep Learning Classifying on the MNIST Dataset/348 - MNIST Learning.mp4 61.9 MB7 - The Field of Data Science Careers in Data Science/23 - Finding the Job What to Expect and What to Look for.mp4 61.3 MB63 - Appendix pandas Fundamentals/480 - Using unique and nunique.mp4 59.8 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - First Regression in Python.mp4 58.7 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/199 - A3 Normality and Homoscedasticity.mp4 58.3 MB58 - Case Study Preprocessing the Absenteeismdata/426 - Using concat in Python.mp4 57.7 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/458 - Preparing the Deployment of the Model through a Module.mp4 57.5 MB2 - The Field of Data Science The Various Data Science Disciplines/7 - Continuing with BI ML and AI.mp4 57.5 MB12 - Probability Distributions/59 - Characteristics of Continuous Distributions.mp4 57.3 MB38 - Advanced Statistical Methods KMeans Clustering/258 - How to Choose the Number of Clusters.mp4 57.1 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/188 - How to Interpret the Regression Table.mp4 57.0 MB34 - Advanced Statistical Methods Linear Regression with sklearn/219 - Feature Selection through Standardization of Weights.mp4 56.5 MB58 - Case Study Preprocessing the Absenteeismdata/419 - Analyzing the Reasons for Absence.mp4 56.5 MB44 - Deep Learning TensorFlow 20 Introduction/300 - How to Install TensorFlow 20.mp4 56.3 MB38 - Advanced Statistical Methods KMeans Clustering/263 - Market Segmentation with Cluster Analysis Part 1.mp4 56.2 MB17 - Statistics Inferential Statistics Fundamentals/97 - The Normal Distribution.mp4 56.2 MB15 - Statistics Descriptive Statistics/73 - Categorical Variables Visualization Techniques.mp4 55.8 MB14 - Part 3 Statistics/70 - Population and Sample.mp4 55.5 MB9 - Part 2 Probability/27 - Frequency.mp4 55.2 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/190 - What is the OLS.mp4 54.9 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/449 - Creating a Summary Table with the Coefficients and Intercept.mp4 54.8 MB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Basic NN Example Part 4.mp4 54.5 MB1 - Part 1 Introduction/2 - What Does the Course Cover.mp4 54.5 MB20 - Statistics Hypothesis Testing/126 - pvalue.mp4 54.2 MB64 - Appendix Working with Text Files in Python/497 - Importing Text Files with open.mp4 53.9 MB44 - Deep Learning TensorFlow 20 Introduction/305 - Outlining the Model with TensorFlow 2.mp4 53.7 MB38 - Advanced Statistical Methods KMeans Clustering/265 - How is Clustering Useful.mp4 53.4 MB12 - Probability Distributions/53 - Types of Probability Distributions.mp4 53.3 MB63 - Appendix pandas Fundamentals/484 - pandas DataFrames Common Attributes.mp4 53.3 MB20 - Statistics Hypothesis Testing/120 - Null vs Alternative Hypothesis.mp4 53.2 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - Business Case Optimization.mp4 53.1 MB64 - Appendix Working with Text Files in Python/512 - Saving Your Data with NumPy Part I npy.mp4 52.6 MB44 - Deep Learning TensorFlow 20 Introduction/306 - Interpreting the Result and Extracting the Weights and Bias.mp4 52.6 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/455 - Saving the Model and Preparing it for Deployment.mp4 52.6 MB64 - Appendix Working with Text Files in Python/496 - Importing Text Files open.mp4 52.3 MB40 - Part 6 Mathematics/276 - Addition and Subtraction of Matrices.mp4 52.1 MB62 - Appendix Additional Python Tools/473 - List Comprehensions.mp4 51.7 MB37 - Advanced Statistical Methods Cluster Analysis/250 - Some Examples of Clusters.mp4 51.3 MB18 - Statistics Inferential Statistics Confidence Intervals/110 - Margin of Error.mp4 50.9 MB13 - Probability Probability in Other Fields/68 - Probability in Statistics.mp4 50.9 MB52 - Deep Learning Conclusion/366 - An overview of CNNs.mp4 50.7 MB15 - Statistics Descriptive Statistics/81 - Mean median and mode.mp4 50.3 MB20 - Statistics Hypothesis Testing/133 - Test for the mean Independent Samples Part 2.mp4 49.2 MB64 - Appendix Working with Text Files in Python/509 - Importing Data with the squeeze Method.mp4 48.3 MB9 - Part 2 Probability/25 - The Basic Probability Formula.mp4 48.2 MB62 - Appendix Additional Python Tools/469 - Using the format Method.mp4 47.6 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/184 - Python Packages Installation.mp4 47.6 MB15 - Statistics Descriptive Statistics/72 - Levels of Measurement.mp4 47.2 MB12 - Probability Distributions/57 - Discrete Distributions The Binomial Distribution.mp4 46.6 MB15 - Statistics Descriptive Statistics/85 - Variance.mp4 46.3 MB50 - Deep Learning Classifying on the MNIST Dataset/342 - MNIST Preprocess the Data Create a Validation Set and Scale It.mp4 45.8 MB18 - Statistics Inferential Statistics Confidence Intervals/103 - What are Confidence Intervals.mp4 45.7 MB50 - Deep Learning Classifying on the MNIST Dataset/350 - MNIST Testing the Model.mp4 45.5 MB5 - The Field of Data Science Popular Data Science Techniques/18 - Real Life Examples of Traditional Methods.mp4 44.8 MB36 - Advanced Statistical Methods Logistic Regression/234 - A Simple Example in Python.mp4 44.6 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/311 - Digging into a Deep Net.mp4 44.4 MB34 - Advanced Statistical Methods Linear Regression with sklearn/208 - Simple Linear Regression with sklearn A StatsModelslike Summary Table.mp4 44.4 MB40 - Part 6 Mathematics/278 - Transpose of a Matrix.mp4 44.3 MB17 - Statistics Inferential Statistics Fundamentals/102 - Estimators and Estimates.mp4 44.3 MB62 - Appendix Additional Python Tools/474 - Anonymous Lambda Functions.mp4 43.5 MB36 - Advanced Statistical Methods Logistic Regression/235 - Logistic vs Logit Function.mp4 43.4 MB50 - Deep Learning Classifying on the MNIST Dataset/346 - MNIST Outline the Model.mp4 43.3 MB58 - Case Study Preprocessing the Absenteeismdata/411 - Importing the Absenteeism Data in Python.mp4 43.3 MB36 - Advanced Statistical Methods Logistic Regression/247 - Testing the Model.mp4 43.2 MB34 - Advanced Statistical Methods Linear Regression with sklearn/220 - Predicting with the Standardized Coefficients.mp4 43.0 MB28 - Python Sequences/166 - Lists.mp4 43.0 MB63 - Appendix pandas Fundamentals/487 - pandas DataFrames Indexing with loc.mp4 43.0 MB58 - Case Study Preprocessing the Absenteeismdata/441 - Final Remarks of this Section.mp4 43.0 MB63 - Appendix pandas Fundamentals/479 - Parameters and Arguments in pandas.mp4 42.8 MB5 - The Field of Data Science Popular Data Science Techniques/16 - Real Life Examples of Business Intelligence BI.mp4 42.8 MB64 - Appendix Working with Text Files in Python/514 - Saving Your Data with NumPy Part III csv.mp4 42.7 MB64 - Appendix Working with Text Files in Python/511 - Saving Your Data with pandas.mp4 42.7 MB34 - Advanced Statistical Methods Linear Regression with sklearn/212 - Calculating the Adjusted RSquared in sklearn.mp4 42.6 MB15 - Statistics Descriptive Statistics/91 - Correlation Coefficient.mp4 41.9 MB60 - Case Study Loading the absenteeismmodule/460 - Deploying the absenteeismmodule Part I.mp4 41.2 MB34 - Advanced Statistical Methods Linear Regression with sklearn/214 - Feature Selection Fregression.mp4 41.1 MB13 - Probability Probability in Other Fields/69 - Probability in Data Science.mp4 40.8 MB29 - Python Iterations/171 - While Loops and Incrementing.mp4 40.7 MB23 - Python Variables and Data Types/145 - Python Strings.mp4 40.4 MB63 - Appendix pandas Fundamentals/475 - Introduction to pandas Series.mp4 40.3 MB64 - Appendix Working with Text Files in Python/510 - Importing Files in Jupyter.mp4 40.3 MB29 - Python Iterations/172 - Lists with the range Function.mp4 40.3 MB58 - Case Study Preprocessing the Absenteeismdata/437 - Extracting the Day of the Week from the Date Column.mp4 39.9 MB36 - Advanced Statistical Methods Logistic Regression/244 - Calculating the Accuracy of the Model.mp4 39.7 MB36 - Advanced Statistical Methods Logistic Regression/238 - An Invaluable Coding Tip.mp4 39.6 MB25 - Python Other Python Operators/154 - Logical and Identity Operators.mp4 39.2 MB40 - Part 6 Mathematics/274 - Arrays in Python A Convenient Way To Represent Matrices.mp4 39.1 MB50 - Deep Learning Classifying on the MNIST Dataset/344 - MNIST Preprocess the Data Shuffle and Batch.mp4 39.0 MB22 - Part 4 Introduction to Python/142 - Prerequisites for Coding in the Jupyter Notebooks.mp4 38.8 MB15 - Statistics Descriptive Statistics/87 - Standard Deviation and Coefficient of Variation.mp4 38.6 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/312 - NonLinearities and their Purpose.mp4 38.5 MB9 - Part 2 Probability/28 - Events and Their Complements.mp4 38.3 MB51 - Deep Learning Business Case Example/353 - Business Case Balancing the Dataset.mp4 38.1 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - The Importance of Working with a Balanced Dataset.mp4 38.1 MB20 - Statistics Hypothesis Testing/127 - Test for the Mean Population Variance Unknown.mp4 37.8 MB17 - Statistics Inferential Statistics Fundamentals/100 - Central Limit Theorem.mp4 37.8 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/398 - Business Case Interpretation.mp4 37.5 MB15 - Statistics Descriptive Statistics/89 - Covariance.mp4 37.4 MB58 - Case Study Preprocessing the Absenteeismdata/440 - Working on Education Children and Pets.mp4 37.4 MB15 - Statistics Descriptive Statistics/79 - Cross Tables and Scatter Plots.mp4 37.3 MB11 - Probability Bayesian Inference/43 - Union of Sets.mp4 36.9 MB12 - Probability Distributions/58 - Discrete Distributions The Poisson Distribution.mp4 36.8 MB10 - Probability Combinatorics/34 - Solving Combinations.mp4 36.7 MB61 - Case Study Analyzing the Predicted Outputs in Tableau/468 - Analyzing Transportation Expense vs Probability in Tableau.mp4 36.7 MB63 - Appendix pandas Fundamentals/477 - Working with Methods in Python Part I.mp4 36.5 MB57 - Case Study Whats Next in the Course/409 - Introducing the Data Set.mp4 36.5 MB58 - Case Study Preprocessing the Absenteeismdata/435 - Analyzing the Dates from the Initial Data Set.mp4 36.4 MB56 - Software Integration/405 - Communication between Software Products through Text Files.mp4 36.1 MB58 - Case Study Preprocessing the Absenteeismdata/413 - Introduction to Terms with Multiple Meanings.mp4 35.9 MB39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps.mp4 35.9 MB58 - Case Study Preprocessing the Absenteeismdata/432 - Creating Checkpoints while Coding in Jupyter.mp4 35.8 MB15 - Statistics Descriptive Statistics/75 - Numerical Variables Frequency Distribution Table.mp4 35.7 MB40 - Part 6 Mathematics/275 - What is a Tensor.mp4 35.1 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - Calculating the Accuracy of the Model.mp4 34.2 MB64 - Appendix Working with Text Files in Python/513 - Saving Your Data with NumPy Part II npz.mp4 34.0 MB42 - Deep Learning Introduction to Neural Networks/293 - Optimization Algorithm 1Parameter Gradient Descent.mp4 34.0 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Business Case A Comment on the Homework.mp4 33.8 MB36 - Advanced Statistical Methods Logistic Regression/242 - Binary Predictors in a Logistic Regression.mp4 33.6 MB28 - Python Sequences/168 - Tuples.mp4 33.6 MB11 - Probability Bayesian Inference/50 - Bayes Law.mp4 33.5 MB34 - Advanced Statistical Methods Linear Regression with sklearn/218 - Feature Scaling Standardization.mp4 33.5 MB44 - Deep Learning TensorFlow 20 Introduction/307 - Customizing a TensorFlow 2 Model.mp4 33.1 MB56 - Software Integration/402 - What are Data Servers Clients Requests and Responses.mp4 33.0 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making Predictions with the Linear Regression.mp4 32.7 MB63 - Appendix pandas Fundamentals/483 - Introduction to pandas DataFrames Part II.mp4 32.2 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/383 - MNIST Loss and Optimization Algorithm.mp4 31.7 MB12 - Probability Distributions/52 - Fundamentals of Probability Distributions.mp4 31.5 MB12 - Probability Distributions/60 - Continuous Distributions The Normal Distribution.mp4 31.5 MB20 - Statistics Hypothesis Testing/124 - Test for the Mean Population Variance Known.mp4 31.4 MB12 - Probability Distributions/61 - Continuous Distributions The Standard Normal Distribution.mp4 31.3 MB57 - Case Study Whats Next in the Course/407 - Game Plan for this Python SQL and Tableau Business Exercise.mp4 31.2 MB11 - Probability Bayesian Inference/41 - Ways Sets Can Interact.mp4 31.1 MB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Basic NN Example Part 3.mp4 30.8 MB2 - The Field of Data Science The Various Data Science Disciplines/6 - Business Analytics Data Analytics and Data Science An Introduction.mp4 30.8 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - Basic NN Example with TF Inputs Outputs Targets Weights Biases.mp4 30.5 MB64 - Appendix Working with Text Files in Python/507 - Working with Excel xlsx Data.mp4 30.3 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/446 - Standardizing the Data.mp4 30.0 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - Basic NN Example with TF Model Output.mp4 29.6 MB20 - Statistics Hypothesis Testing/131 - Test for the mean Independent Samples Part 1.mp4 29.6 MB29 - Python Iterations/175 - How to Iterate over Dictionaries.mp4 29.1 MB11 - Probability Bayesian Inference/46 - The Conditional Probability Formula.mp4 28.9 MB63 - Appendix pandas Fundamentals/481 - Using sortvalues.mp4 28.6 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - Basic NN Example with TF Loss Function and Gradient Descent.mp4 28.6 MB11 - Probability Bayesian Inference/40 - Sets and Events.mp4 28.5 MB28 - Python Sequences/167 - List Slicing.mp4 28.4 MB18 - Statistics Inferential Statistics Confidence Intervals/106 - Confidence Interval Clarifications.mp4 28.3 MB17 - Statistics Inferential Statistics Fundamentals/96 - What is a Distribution.mp4 28.2 MB5 - The Field of Data Science Popular Data Science Techniques/21 - Real Life Examples of Machine Learning ML.mp4 28.1 MB15 - Statistics Descriptive Statistics/83 - Skewness.mp4 28.0 MB10 - Probability Combinatorics/30 - Permutations and How to Use Them.mp4 27.8 MB5 - The Field of Data Science Popular Data Science Techniques/12 - Real Life Examples of Traditional Data.mp4 27.7 MB58 - Case Study Preprocessing the Absenteeismdata/439 - Analyzing Several Straightforward Columns for this Exercise.mp4 27.5 MB29 - Python Iterations/173 - Conditional Statements and Loops.mp4 27.3 MB10 - Probability Combinatorics/37 - Combinatorics in RealLife The Lottery.mp4 26.8 MB26 - Python Conditional Statements/157 - The ELIF Statement.mp4 26.8 MB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/296 - Basic NN Example Part 2.mp4 26.6 MB20 - Statistics Hypothesis Testing/123 - Type I Error and Type II Error.mp4 26.6 MB11 - Probability Bayesian Inference/49 - The Multiplication Law.mp4 26.4 MB12 - Probability Distributions/65 - Continuous Distributions The Logistic Distribution.mp4 26.2 MB18 - Statistics Inferential Statistics Confidence Intervals/115 - Confidence intervals Two means Independent Samples Part 2.mp4 26.2 MB64 - Appendix Working with Text Files in Python/492 - Importing Data in Python Principles.mp4 26.2 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/330 - Learning Rate Schedules or How to Choose the Optimal Learning Rate.mp4 26.2 MB40 - Part 6 Mathematics/279 - Dot Product.mp4 26.1 MB12 - Probability Distributions/64 - Continuous Distributions The Exponential Distribution.mp4 25.5 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/452 - Interpreting the Coefficients of the Logistic Regression.mp4 25.5 MB39 - Advanced Statistical Methods Other Types of Clustering/269 - Dendrogram.mp4 25.5 MB18 - Statistics Inferential Statistics Confidence Intervals/108 - Confidence Intervals Population Variance Unknown Tscore.mp4 25.3 MB36 - Advanced Statistical Methods Logistic Regression/239 - Understanding Logistic Regression Tables.mp4 25.3 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/372 - TensorFlow Intro.mp4 24.9 MB2 - The Field of Data Science The Various Data Science Disciplines/4 - Data Science and Business Buzzwords Why are there so Many.mp4 24.9 MB10 - Probability Combinatorics/38 - A Recap of Combinatorics.mp4 24.5 MB50 - Deep Learning Classifying on the MNIST Dataset/341 - MNIST Importing the Relevant Packages and Loading the Data.mp4 24.1 MB46 - Deep Learning Overfitting/319 - Underfitting and Overfitting for Classification.mp4 24.1 MB64 - Appendix Working with Text Files in Python/499 - Importing csv Files Part II.mp4 24.0 MB22 - Part 4 Introduction to Python/137 - Introduction to Programming.mp4 23.7 MB62 - Appendix Additional Python Tools/470 - Iterating Over Range Objects.mp4 23.7 MB42 - Deep Learning Introduction to Neural Networks/294 - Optimization Algorithm nParameter Gradient Descent.mp4 23.4 MB42 - Deep Learning Introduction to Neural Networks/288 - The Linear model with Multiple Inputs and Multiple Outputs.mp4 22.8 MB11 - Probability Bayesian Inference/47 - The Law of Total Probability.mp4 22.8 MB44 - Deep Learning TensorFlow 20 Introduction/301 - TensorFlow Outline and Comparison with Other Libraries.mp4 22.7 MB40 - Part 6 Mathematics/273 - Linear Algebra and Geometry.mp4 22.4 MB11 - Probability Bayesian Inference/45 - Dependence and Independence of Sets.mp4 22.3 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/381 - MNIST Relevant Packages.mp4 22.3 MB52 - Deep Learning Conclusion/368 - An Overview of nonNN Approaches.mp4 22.3 MB29 - Python Iterations/170 - For Loops.mp4 22.2 MB62 - Appendix Additional Python Tools/471 - Introduction to Nested For Loops.mp4 22.1 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - The Linear Regression Model.mp4 21.9 MB12 - Probability Distributions/56 - Discrete Distributions The Bernoulli Distribution.mp4 21.8 MB37 - Advanced Statistical Methods Cluster Analysis/249 - Introduction to Cluster Analysis.mp4 21.7 MB10 - Probability Combinatorics/35 - Symmetry of Combinations.mp4 21.6 MB18 - Statistics Inferential Statistics Confidence Intervals/107 - Students T Distribution.mp4 21.6 MB64 - Appendix Working with Text Files in Python/501 - Importing Data with indexcol.mp4 21.6 MB44 - Deep Learning TensorFlow 20 Introduction/302 - TensorFlow 1 vs TensorFlow 2.mp4 21.4 MB10 - Probability Combinatorics/32 - Solving Variations with Repetition.mp4 21.3 MB50 - Deep Learning Classifying on the MNIST Dataset/347 - MNIST Select the Loss and the Optimizer.mp4 21.1 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Exploring the Problem with a Machine Learning Mindset.mp4 20.9 MB10 - Probability Combinatorics/36 - Solving Combinations with Separate Sample Spaces.mp4 20.8 MB58 - Case Study Preprocessing the Absenteeismdata/429 - Reordering Columns in a Pandas DataFrame in Python.mp4 20.5 MB42 - Deep Learning Introduction to Neural Networks/285 - Types of Machine Learning.mp4 19.9 MB34 - Advanced Statistical Methods Linear Regression with sklearn/211 - Multiple Linear Regression with sklearn.mp4 19.9 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/385 - MNIST Batching and Early Stopping.mp4 19.8 MB22 - Part 4 Introduction to Python/138 - Why Python.mp4 19.8 MB15 - Statistics Descriptive Statistics/77 - The Histogram.mp4 19.7 MB41 - Part 7 Deep Learning/282 - What to Expect from this Part.mp4 19.3 MB51 - Deep Learning Business Case Example/356 - Business Case Load the Preprocessed Data.mp4 19.2 MB18 - Statistics Inferential Statistics Confidence Intervals/113 - Confidence intervals Two means Independent Samples Part 1.mp4 19.2 MB57 - Case Study Whats Next in the Course/408 - The Business Task.mp4 19.1 MB35 - Advanced Statistical Methods Practical Example Linear Regression/227 - Practical Example Linear Regression Part 3.mp4 19.1 MB38 - Advanced Statistical Methods KMeans Clustering/256 - Clustering Categorical Data.mp4 19.0 MB64 - Appendix Working with Text Files in Python/493 - Plain Text Files Flat Files and More.mp4 18.9 MB58 - Case Study Preprocessing the Absenteeismdata/415 - Using a Statistical Approach towards the Solution to the Exercise.mp4 18.7 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/373 - Actual Introduction to TensorFlow.mp4 18.4 MB36 - Advanced Statistical Methods Logistic Regression/241 - What do the Odds Actually Mean.mp4 18.4 MB5 - The Field of Data Science Popular Data Science Techniques/14 - Real Life Examples of Big Data.mp4 18.2 MB27 - Python Python Functions/165 - Builtin Functions in Python.mp4 18.1 MB49 - Deep Learning Preprocessing/336 - Standardization.mp4 18.0 MB30 - Python Advanced Python Tools/179 - Importing Modules in Python.mp4 17.8 MB11 - Probability Bayesian Inference/48 - The Additive Rule.mp4 17.7 MB63 - Appendix pandas Fundamentals/482 - Introduction to pandas DataFrames Part I.mp4 17.7 MB10 - Probability Combinatorics/33 - Solving Variations without Repetition.mp4 17.7 MB64 - Appendix Working with Text Files in Python/490 - Structured SemiStructured and Unstructured Data.mp4 17.4 MB40 - Part 6 Mathematics/271 - What is a Matrix.mp4 17.3 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/310 - What is a Deep Net.mp4 17.3 MB11 - Probability Bayesian Inference/42 - Intersection of Sets.mp4 17.3 MB64 - Appendix Working with Text Files in Python/488 - An Introduction to Working with Files in Python.mp4 17.3 MB38 - Advanced Statistical Methods KMeans Clustering/261 - To Standardize or not to Standardize.mp4 16.8 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/191 - RSquared.mp4 16.7 MB12 - Probability Distributions/63 - Continuous Distributions The ChiSquared Distribution.mp4 16.7 MB2 - The Field of Data Science The Various Data Science Disciplines/5 - What is the difference between Analysis and Analytics.mp4 16.6 MB51 - Deep Learning Business Case Example/361 - Business Case Testing the Model.mp4 16.6 MB23 - Python Variables and Data Types/143 - Variables.mp4 16.5 MB38 - Advanced Statistical Methods KMeans Clustering/253 - KMeans Clustering.mp4 16.3 MB
65 - Bonus Lecture/517 - 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 16.3 MB64 - Appendix Working with Text Files in Python/491 - Text Files and Data Connectivity.mp4 16.3 MB63 - Appendix pandas Fundamentals/478 - Working with Methods in Python Part II.mp4 16.2 MB11 - Probability Bayesian Inference/44 - Mutually Exclusive Sets.mp4 15.9 MB46 - Deep Learning Overfitting/318 - What is Overfitting.mp4 15.9 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/374 - Types of File Formats supporting Tensors.mp4 15.8 MB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Basic NN Example Part 1.mp4 15.7 MB12 - Probability Distributions/55 - Discrete Distributions The Uniform Distribution.mp4 15.7 MB46 - Deep Learning Overfitting/323 - Early Stopping or When to Stop Training.mp4 15.6 MB24 - Python Basic Python Syntax/146 - Using Arithmetic Operators in Python.mp4 15.6 MB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/445 - Selecting the Inputs for the Logistic Regression.mp4 15.5 MB44 - Deep Learning TensorFlow 20 Introduction/304 - Types of File Formats Supporting TensorFlow.mp4 15.5 MB34 - Advanced Statistical Methods Linear Regression with sklearn/205 - What is sklearn and How is it Different from Other Packages.mp4 15.5 MB38 - Advanced Statistical Methods KMeans Clustering/260 - Pros and Cons of KMeans Clustering.mp4 15.5 MB36 - Advanced Statistical Methods Logistic Regression/236 - Building a Logistic Regression.mp4 15.4 MB10 - Probability Combinatorics/31 - Simple Operations with Factorials.mp4 15.3 MB42 - Deep Learning Introduction to Neural Networks/283 - Introduction to Neural Networks.mp4 15.2 MB46 - Deep Learning Overfitting/321 - Training Validation and Test Datasets.mp4 15.1 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/327 - Stochastic Gradient Descent.mp4 15.1 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/315 - Backpropagation.mp4 15.0 MB27 - Python Python Functions/160 - How to Create a Function with a Parameter.mp4 14.9 MB52 - Deep Learning Conclusion/363 - Summary on What Youve Learned.mp4 14.9 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/187 - Using Seaborn for Graphs.mp4 14.9 MB12 - Probability Distributions/62 - Continuous Distributions The Students T Distribution.mp4 14.8 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/198 - A2 No Endogeneity.mp4 14.6 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/189 - Decomposition of Variability.mp4 14.6 MB12 - Probability Distributions/54 - Characteristics of Discrete Distributions.mp4 14.5 MB17 - Statistics Inferential Statistics Fundamentals/98 - The Standard Normal Distribution.mp4 14.5 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/314 - Activation Functions Softmax Activation.mp4 14.3 MB39 - Advanced Statistical Methods Other Types of Clustering/268 - Types of Clustering.mp4 14.3 MB64 - Appendix Working with Text Files in Python/495 - Common Naming Conventions.mp4 14.2 MB42 - Deep Learning Introduction to Neural Networks/292 - Common Objective Functions CrossEntropy Loss.mp4 14.2 MB46 - Deep Learning Overfitting/320 - What is Validation.mp4 14.0 MB47 - Deep Learning Initialization/324 - What is Initialization.mp4 13.8 MB37 - Advanced Statistical Methods Cluster Analysis/251 - Difference between Classification and Clustering.mp4 13.8 MB40 - Part 6 Mathematics/272 - Scalars and Vectors.mp4 13.5 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/313 - Activation Functions.mp4 13.5 MB64 - Appendix Working with Text Files in Python/489 - File vs File Object Reading vs Parsing Data.mp4 13.4 MB50 - Deep Learning Classifying on the MNIST Dataset/340 - MNIST How to Tackle the MNIST.mp4 13.4 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/332 - Adaptive Learning Rate Schedules AdaGrad and RMSprop.mp4 13.3 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/380 - MNIST How to Tackle the MNIST.mp4 13.0 MB22 - Part 4 Introduction to Python/139 - Why Jupyter.mp4 12.8 MB49 - Deep Learning Preprocessing/334 - Preprocessing Introduction.mp4 12.8 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/316 - Backpropagation Picture.mp4 12.8 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/200 - A4 No Autocorrelation.mp4 12.5 MB30 - Python Advanced Python Tools/176 - Object Oriented Programming.mp4 12.4 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/195 - Test for Significance of the Model FTest.mp4 12.4 MB27 - Python Python Functions/161 - Defining a Function in Python Part II.mp4 12.1 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/201 - A5 No Multicollinearity.mp4 11.8 MB49 - Deep Learning Preprocessing/338 - Binary and OneHot Encoding.mp4 11.7 MB18 - Statistics Inferential Statistics Confidence Intervals/117 - Confidence intervals Two means Independent Samples Part 3.mp4 11.6 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/333 - Adam Adaptive Moment Estimation.mp4 11.5 MB42 - Deep Learning Introduction to Neural Networks/289 - Graphical Representation of Simple Neural Networks.mp4 11.3 MB34 - Advanced Statistical Methods Linear Regression with sklearn/216 - Creating a Summary Table with Pvalues.mp4 11.2 MB52 - Deep Learning Conclusion/367 - An Overview of RNNs.mp4 11.1 MB36 - Advanced Statistical Methods Logistic Regression/246 - Underfitting and Overfitting.mp4 11.1 MB42 - Deep Learning Introduction to Neural Networks/286 - The Linear Model Linear Algebraic Version.mp4 11.1 MB42 - Deep Learning Introduction to Neural Networks/287 - The Linear Model with Multiple Inputs.mp4 11.0 MB42 - Deep Learning Introduction to Neural Networks/284 - Training the Model.mp4 11.0 MB36 - Advanced Statistical Methods Logistic Regression/233 - Introduction to Logistic Regression.mp4 10.9 MB23 - Python Variables and Data Types/144 - Numbers and Boolean Values in Python.mp4 10.7 MB58 - Case Study Preprocessing the Absenteeismdata/424 - More on Dummy Variables A Statistical Perspective.mp4 10.6 MB17 - Statistics Inferential Statistics Fundamentals/101 - Standard error.mp4 10.6 MB27 - Python Python Functions/163 - Conditional Statements and Functions.mp4 10.3 MB40 - Part 6 Mathematics/277 - Errors when Adding Matrices.mp4 10.0 MB10 - Probability Combinatorics/29 - Fundamentals of Combinatorics.mp4 9.8 MB22 - Part 4 Introduction to Python/141 - Understanding Jupyters Interface the Notebook Dashboard.mp4 9.8 MB46 - Deep Learning Overfitting/322 - NFold Cross Validation.mp4 9.8 MB53 - Appendix Deep Learning TensorFlow 1 Introduction/370 - How to Install TensorFlow 1.mp4 9.2 MB47 - Deep Learning Initialization/325 - Types of Simple Initializations.mp4 9.2 MB26 - Python Conditional Statements/156 - The ELSE Statement.mp4 9.2 MB26 - Python Conditional Statements/155 - The IF Statement.mp4 9.2 MB44 - Deep Learning TensorFlow 20 Introduction/303 - A Note on TensorFlow 2 Syntax.mp4 9.1 MB
12 - Probability Distributions/66 - FIFA19-post.csv 9.1 MB12 - Probability Distributions/66 - FIFA19.csv 9.1 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/192 - Multiple Linear Regression.mp4 8.7 MB42 - Deep Learning Introduction to Neural Networks/290 - What is the Objective Function.mp4 8.6 MB47 - Deep Learning Initialization/326 - StateoftheArt Method Xavier Glorot Initialization.mp4 8.6 MB34 - Advanced Statistical Methods Linear Regression with sklearn/222 - Underfitting and Overfitting.mp4 8.6 MB42 - Deep Learning Introduction to Neural Networks/291 - Common Objective Functions L2norm Loss.mp4 8.2 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/309 - What is a Layer.mp4 8.2 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/196 - OLS Assumptions.mp4 8.2 MB58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 8.0 MB34 - Advanced Statistical Methods Linear Regression with sklearn/206 - How are we Going to Approach this Section.mp4 7.9 MB64 - Appendix Working with Text Files in Python/494 - Text Files of Fixed Width.mp4 7.6 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/399 - Business Case Testing the Model.mp4 7.5 MB37 - Advanced Statistical Methods Cluster Analysis/252 - Math Prerequisites.mp4 7.5 MB49 - Deep Learning Preprocessing/337 - Preprocessing Categorical Data.mp4 7.5 MB30 - Python Advanced Python Tools/178 - What is the Standard Library.mp4 7.5 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/329 - Momentum.mp4 7.4 MB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/379 - MNIST What is the MNIST Dataset.mp4 7.3 MB
2 - The Field of Data Science The Various Data Science Disciplines/7 - 365-DataScience.png 7.3 MB2 - The Field of Data Science The Various Data Science Disciplines/8 - 365-DataScience.png 7.3 MB26 - Python Conditional Statements/158 - A Note on Boolean Values.mp4 7.1 MB52 - Deep Learning Conclusion/364 - Whats Further out there in terms of Machine Learning.mp4 7.1 MB50 - Deep Learning Classifying on the MNIST Dataset/339 - MNIST The Dataset.mp4 7.0 MB25 - Python Other Python Operators/153 - Comparison Operators.mp4 6.9 MB29 - Python Iterations/174 - Conditional Statements Functions and Loops.mp4 6.8 MB55 - Appendix Deep Learning TensorFlow 1 Business Case/391 - Business Case Outlining the Solution.mp4 6.6 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/182 - Correlation vs Regression.mp4 5.9 MB31 - Part 5 Advanced Statistical Methods in Python/180 - Introduction to Regression Analysis.mp4 5.8 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/328 - Problems with Gradient Descent.mp4 5.6 MB38 - Advanced Statistical Methods KMeans Clustering/262 - Relationship between Clustering and Regression.mp4 5.6 MB17 - Statistics Inferential Statistics Fundamentals/95 - Introduction.mp4 5.4 MB27 - Python Python Functions/159 - Defining a Function in Python.mp4 5.4 MB27 - Python Python Functions/162 - How to Use a Function within a Function.mp4 5.4 MB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/197 - A1 Linearity.mp4 5.3 MB27 - Python Python Functions/164 - Functions Containing a Few Arguments.mp4 5.1 MB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/331 - Learning Rate Schedules Visualized.mp4 5.0 MB49 - Deep Learning Preprocessing/335 - Types of Basic Preprocessing.mp4 4.9 MB51 - Deep Learning Business Case Example/352 - Business Case Outlining the Solution.mp4 4.7 MB24 - Python Basic Python Syntax/152 - Structuring with Indentation.mp4 4.7 MB24 - Python Basic Python Syntax/147 - The Double Equality Sign.mp4 4.4 MB24 - Python Basic Python Syntax/149 - Add Comments.mp4 4.0 MB24 - Python Basic Python Syntax/151 - Indexing Elements.mp4 3.8 MB32 - Advanced Statistical Methods Linear Regression with StatsModels/183 - Geometrical Representation of the Linear Regression Model.mp4 3.3 MB30 - Python Advanced Python Tools/177 - Modules and Packages.mp4 3.1 MB64 - Appendix Working with Text Files in Python/516 - Working with Text Files in Python Conclusion.mp4 3.1 MB24 - Python Basic Python Syntax/148 - How to Reassign Values.mp4 3.0 MB22 - Part 4 Introduction to Python/137 - Introduction-to-Python-Course-Notes.pdf 2.3 MB23 - Python Variables and Data Types/143 - Introduction-to-Python-Course-Notes.pdf 2.3 MB19 - Statistics Practical Example Inferential Statistics/119 - 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB24 - Python Basic Python Syntax/150 - Understanding Line Continuation.mp4 1.8 MB19 - Statistics Practical Example Inferential Statistics/118 - 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.8 MB19 - Statistics Practical Example Inferential Statistics/119 - 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.8 MB20 - Statistics Hypothesis Testing/126 - Online-p-value-calculator.pdf 1.2 MB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/309 - Course-Notes-Section-6.pdf 958.9 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/310 - Course-Notes-Section-6.pdf 958.9 kB11 - Probability Bayesian Inference/51 - CDS-2017-2018-Hamilton.pdf 865.6 kB35 - Advanced Statistical Methods Practical Example Linear Regression/231 - sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB51 - Deep Learning Business Case Example/351 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Audiobooks-data.csv 727.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - Audiobooks-data.csv 727.8 kB35 - Advanced Statistical Methods Practical Example Linear Regression/231 - sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 715.1 kB20 - Statistics Hypothesis Testing/120 - Course-notes-hypothesis-testing.pdf 672.2 kB20 - Statistics Hypothesis Testing/122 - Course-notes-hypothesis-testing.pdf 672.2 kB64 - Appendix Working with Text Files in Python/488 - Common-Naming-Conventions.pdf 659.2 kB64 - Appendix Working with Text Files in Python/495 - Common-Naming-Conventions.pdf 659.2 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Shortcuts-for-Jupyter.pdf 634.0 kB44 - Deep Learning TensorFlow 20 Introduction/300 - Shortcuts-for-Jupyter.pdf 634.0 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/373 - Shortcuts-for-Jupyter.pdf 634.0 kB42 - Deep Learning Introduction to Neural Networks/283 - Course-Notes-Section-2.pdf 592.0 kB42 - Deep Learning Introduction to Neural Networks/284 - Course-Notes-Section-2.pdf 592.0 kB14 - Part 3 Statistics/70 - Course-notes-descriptive-statistics.pdf 493.8 kB15 - Statistics Descriptive Statistics/71 - Course-notes-descriptive-statistics.pdf 493.8 kB12 - Probability Distributions/52 - Course-Notes-Probability-Distributions.pdf 475.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/229 - sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 417.4 kB35 - Advanced Statistical Methods Practical Example Linear Regression/229 - sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 406.8 kB11 - Probability Bayesian Inference/40 - Course-Notes-Bayesian-Inference.pdf 395.3 kB17 - Statistics Inferential Statistics Fundamentals/95 - Course-notes-inferential-statistics.pdf 391.5 kB17 - Statistics Inferential Statistics Fundamentals/96 - Course-notes-inferential-statistics.pdf 391.5 kB9 - Part 2 Probability/25 - Course-Notes-Basic-Probability.pdf 380.0 kB35 - Advanced Statistical Methods Practical Example Linear Regression/228 - sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 379.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 359.9 kB35 - Advanced Statistical Methods Practical Example Linear Regression/228 - sklearn-Dummies-and-VIF-Exercise.ipynb 352.9 kB12 - Probability Distributions/59 - Solving-Integrals.pdf 352.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 351.8 kB35 - Advanced Statistical Methods Practical Example Linear Regression/225 - sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 343.7 kB36 - Advanced Statistical Methods Logistic Regression/233 - Course-Notes-Logistic-Regression.pdf 343.2 kB36 - Advanced Statistical Methods Logistic Regression/234 - Course-Notes-Logistic-Regression.pdf 343.2 kB35 - Advanced Statistical Methods Practical Example Linear Regression/225 - sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB2 - The Field of Data Science The Various Data Science Disciplines/6 - 365-DataScience-Diagram.pdf 330.8 kB2 - The Field of Data Science The Various Data Science Disciplines/7 - 365-DataScience-Diagram.pdf 330.8 kB13 - Probability Probability in Other Fields/69 - Probability-Cheat-Sheet.pdf 328.0 kB31 - Part 5 Advanced Statistical Methods in Python/180 - Course-notes-regression-analysis.pdf 319.7 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - Course-notes-regression-analysis.pdf 319.7 kB1 - Part 1 Introduction/3 - FAQ-The-Data-Science-Course.pdf 313.4 kB15 - Statistics Descriptive Statistics/74 - Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB15 - Statistics Descriptive Statistics/78 - Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB10 - Probability Combinatorics/39 - Additional-Exercises-Combinatorics-Solutions.pdf 251.6 kB10 - Probability Combinatorics/29 - Course-Notes-Combinatorics.pdf 231.5 kB35 - Advanced Statistical Methods Practical Example Linear Regression/224 - 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/225 - 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/228 - 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/229 - 1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods Practical Example Linear Regression/231 - 1.04.Real-life-example.csv 225.1 kB64 - Appendix Working with Text Files in Python/505 - Lending-company.json 218.7 kB37 - Advanced Statistical Methods Cluster Analysis/249 - Course-Notes-Cluster-Analysis.pdf 213.7 kB37 - Advanced Statistical Methods Cluster Analysis/250 - Course-Notes-Cluster-Analysis.pdf 213.7 kB10 - Probability Combinatorics/34 - Combinations-With-Repetition.pdf 212.4 kB13 - Probability Probability in Other Fields/67 - Probability-in-Finance-Solutions.pdf 188.9 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/317 - Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB35 - Advanced Statistical Methods Practical Example Linear Regression/224 - sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 175.5 kB35 - Advanced Statistical Methods Practical Example Linear Regression/224 - sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 170.9 kB63 - Appendix pandas Fundamentals/475 - Sales-products.csv 155.9 kB63 - Appendix pandas Fundamentals/487 - Sales-products.csv 155.9 kB16 - Statistics Practical Example Descriptive Statistics/93 - 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 150.0 kB16 - Statistics Practical Example Descriptive Statistics/94 - 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 149.9 kB12 - Probability Distributions/58 - Poisson-Expected-Value-and-Variance.pdf 149.5 kB12 - Probability Distributions/60 - Normal-Distribution-Exp-and-Var.pdf 147.5 kB58 - Case Study Preprocessing the Absenteeismdata/410 - data-preprocessing-homework.pdf 137.7 kB16 - Statistics Practical Example Descriptive Statistics/94 - 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 123.2 kB63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Solutions.ipynb 121.2 kB63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Solutions.ipynb 121.2 kB64 - Appendix Working with Text Files in Python/498 - Lending-company-single-column-data.csv 117.2 kB63 - Appendix pandas Fundamentals/475 - Lending-company.csv 115.1 kB63 - Appendix pandas Fundamentals/487 - Lending-company.csv 115.1 kB64 - Appendix Working with Text Files in Python/498 - Lending-company.csv 115.1 kB36 - Advanced Statistical Methods Logistic Regression/248 - Testing-the-Model-Solution.ipynb 113.8 kB13 - Probability Probability in Other Fields/67 - Probability-in-Finance-Homework.pdf 113.3 kB10 - Probability Combinatorics/39 - Additional-Exercises-Combinatorics.pdf 109.1 kB64 - Appendix Working with Text Files in Python/507 - Lending-company.xlsx 95.3 kB10 - Probability Combinatorics/35 - Symmetry-Explained.pdf 87.1 kB44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 85.7 kB44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-example-All-exercises.ipynb 85.6 kB44 - Deep Learning TensorFlow 20 Introduction/307 - TensorFlow-Minimal-example-complete-with-comments.ipynb 84.3 kB36 - Advanced Statistical Methods Logistic Regression/245 - Calculating-the-Accuracy-of-the-Model-Solution.ipynb 83.2 kB44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 79.4 kB44 - Deep Learning TensorFlow 20 Introduction/307 - TensorFlow-Minimal-example-complete.ipynb 78.7 kB44 - Deep Learning TensorFlow 20 Introduction/306 - TensorFlow-Minimal-example-Part3.ipynb 78.4 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.c.Solution.ipynb 71.8 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-1-Solution.ipynb 70.7 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-5-Solution.ipynb 70.5 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.a.Solution.ipynb 69.5 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-3.b.Solution.ipynb 69.3 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-4-Solution.ipynb 68.1 kB60 - Case Study Loading the absenteeismmodule/459 - Absenteeism-Exercise-Integration.ipynb 63.8 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-6-Solution.ipynb 63.2 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-6.ipynb 63.2 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-Exercise-2-Solution.ipynb 62.9 kB64 - Appendix Working with Text Files in Python/512 - Lending-Company-Saving.csv 59.8 kB21 - Statistics Practical Example Hypothesis Testing/135 - 4.10.Hypothesis-testing-section-practical-example.xlsx 53.1 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 51.2 kB21 - Statistics Practical Example Hypothesis Testing/136 - 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 45.3 kB21 - Statistics Practical Example Hypothesis Testing/136 - 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 44.7 kB42 - Deep Learning Introduction to Neural Networks/293 - GD-function-example.xlsx 43.4 kB15 - Statistics Descriptive Statistics/74 - 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 42.1 kB15 - Statistics Descriptive Statistics/80 - 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 41.4 kB15 - Statistics Descriptive Statistics/83 - 2.8.Skewness-lesson.xlsx 35.5 kB58 - Case Study Preprocessing the Absenteeismdata/410 - Absenteeism-data.csv 32.8 kB63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Exercises.ipynb 31.7 kB63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Exercises.ipynb 31.7 kB15 - Statistics Descriptive Statistics/73 - 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31.5 kB11 - Probability Bayesian Inference/51 - Bayesian-Homework-Solutions.pdf 31.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/220 - sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 30.5 kB64 - Appendix Working with Text Files in Python/502 - Lending-Company-Numeric-Data.csv 30.2 kB15 - Statistics Descriptive Statistics/90 - 2.11.Covariance-exercise-solution.xlsx 30.2 kB15 - Statistics Descriptive Statistics/92 - 2.12.Correlation-exercise-solution.xlsx 30.2 kB15 - Statistics Descriptive Statistics/92 - 2.12.Correlation-exercise.xlsx 30.0 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Absenteeism-preprocessed.csv 29.8 kB58 - Case Study Preprocessing the Absenteeismdata/410 - df-preprocessed.csv 29.8 kB64 - Appendix Working with Text Files in Python/502 - Lending-Company-Numeric-Data-NAN.csv 29.3 kB34 - Advanced Statistical Methods Linear Regression with sklearn/208 - sklearn-Simple-Linear-Regression-with-comments.ipynb 29.0 kB44 - Deep Learning TensorFlow 20 Introduction/308 - TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.6 kB64 - Appendix Working with Text Files in Python/488 - Working-with-Text-Files-Lectures.ipynb 28.2 kB64 - Appendix Working with Text Files in Python/516 - Working-with-Text-Files-Lectures.ipynb 28.2 kB11 - Probability Bayesian Inference/51 - Bayesian-Homework.pdf 27.9 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.6 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 27.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/210 - Simple-Linear-Regression-with-sklearn-Exercise-Solution.ipynb 27.2 kB12 - Probability Distributions/66 - A Practical Example of Probability Distributions English.srt 27.1 kB16 - Statistics Practical Example Descriptive Statistics/93 - Practical Example Descriptive Statistics English.srt 27.0 kB15 - Statistics Descriptive Statistics/79 - 2.6.Cross-table-and-scatter-plot.xlsx 26.7 kB34 - Advanced Statistical Methods Linear Regression with sklearn/208 - sklearn-Simple-Linear-Regression.ipynb 26.7 kB18 - Statistics Inferential Statistics Confidence Intervals/104 - 3.9.The-z-table.xlsx 26.2 kB18 - Statistics Inferential Statistics Confidence Intervals/105 - 3.9.The-z-table.xlsx 26.2 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 26.2 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 26.1 kB62 - Appendix Additional Python Tools/469 - Additional-Python-Tools-Solutions.ipynb 26.1 kB62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Solutions.ipynb 26.1 kB11 - Probability Bayesian Inference/51 - A Practical Example of Bayesian Inference English.srt 25.8 kB15 - Statistics Descriptive Statistics/89 - 2.11.Covariance-lesson.xlsx 25.5 kB64 - Appendix Working with Text Files in Python/504 - Importing-Text-Data-DSc-Solution.ipynb 25.0 kB17 - Statistics Inferential Statistics Fundamentals/99 - 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.6 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 24.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/220 - sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.6 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 22.3 kB
1 - Part 1 Introduction/3 - Download All Resources and Important FAQ.html 21.9 kB63 - Appendix pandas Fundamentals/475 - pandas-Fundamentals-Lectures.ipynb 21.8 kB63 - Appendix pandas Fundamentals/487 - pandas-Fundamentals-Lectures.ipynb 21.8 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 21.1 kB14 - Part 3 Statistics/70 - Statistics-Glossary.xlsx 20.8 kB15 - Statistics Descriptive Statistics/90 - 2.11.Covariance-exercise.xlsx 20.7 kB12 - Probability Distributions/66 - Daily-Views-post.xlsx 20.7 kB64 - Appendix Working with Text Files in Python/509 - Importing-Data-with-the-pandas-Squeeze-Method.ipynb 20.6 kB15 - Statistics Descriptive Statistics/71 - Glossary.xlsx 20.4 kB15 - Statistics Descriptive Statistics/84 - 2.8.Skewness-exercise-solution.xlsx 20.2 kB51 - Deep Learning Business Case Example/358 - TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 20.2 kB36 - Advanced Statistical Methods Logistic Regression/240 - 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Heatmaps-with-comments.ipynb 18.1 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - TensorFlow-MNIST-around-98-percent-accuracy.ipynb 18.1 kB19 - Statistics Practical Example Inferential Statistics/118 - Practical Example Inferential Statistics English.srt 17.8 kB15 - Statistics Descriptive Statistics/78 - 2.5.The-Histogram-exercise-solution.xlsx 17.5 kB51 - Deep Learning Business Case Example/354 - Business Case Preprocessing the Data English.srt 17.5 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - Business Case Preprocessing English.srt 17.4 kB64 - Appendix Working with Text Files in Python/496 - Importing Text Files open English.srt 17.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 17.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/219 - SKLEAR-1.IPY 17.2 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - TensorFlow-MNIST-All-Exercises.ipynb 17.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/216 - sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb 17.0 kB34 - Advanced Statistical Methods Linear Regression with sklearn/221 - sklearn-Feature-Scaling-Exercise-Solution.ipynb 16.7 kB15 - Statistics Descriptive Statistics/80 - 2.6.Cross-table-and-scatter-plot-exercise.xlsx 16.7 kB62 - Appendix Additional Python Tools/473 - List Comprehensions English.srt 16.4 kB18 - Statistics Inferential Statistics Confidence Intervals/108 - 3.11.The-t-table.xlsx 16.2 kB18 - Statistics Inferential Statistics Confidence Intervals/109 - 3.11.The-t-table.xlsx 16.2 kB62 - Appendix Additional Python Tools/469 - Using the format Method English.srt 16.2 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 16.2 kB12 - Probability Distributions/66 - Customers-Membership-post.xlsx 16.0 kB2 - The Field of Data Science The Various Data Science Disciplines/7 - Continuing with BI ML and AI English.srt 15.9 kB15 - Statistics Descriptive Statistics/78 - 2.5.The-Histogram-exercise.xlsx 15.9 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/388 - TensorFlow-MNIST-Exercises-All.ipynb 15.8 kB34 - Advanced Statistical Methods Linear Regression with sklearn/217 - sklearn-Multiple-Linear-Regression-Exercise-Solution.ipynb 15.8 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.7 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 15.7 kB38 - Advanced Statistical Methods KMeans Clustering/267 - Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.7 kB15 - Statistics Descriptive Statistics/74 - 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.6 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.6 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 15.5 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/219 - sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2.ipynb 15.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 2.TensorFlow-MNIST-Depth-Solution.ipynb 15.2 kB35 - Advanced Statistical Methods Practical Example Linear Regression/229 - Practical Example Linear Regression Part 4 English.srt 15.2 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 1.TensorFlow-MNIST-Width-Solution.ipynb 15.2 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 15.1 kB20 - Statistics Hypothesis Testing/127 - 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.9 kB50 - Deep Learning Classifying on the MNIST Dataset/350 - TensorFlow-MNIST-complete-with-comments.ipynb 14.9 kB5 - The Field of Data Science Popular Data Science Techniques/17 - Techniques for Working with Traditional Methods English.srt 14.8 kB20 - Statistics Hypothesis Testing/130 - 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.7 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB40 - Part 6 Mathematics/281 - Why is Linear Algebra Useful English.srt 14.7 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.7 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.6 kB18 - Statistics Inferential Statistics Confidence Intervals/112 - 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.6 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 14.5 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 14.4 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 1.TensorFlow-MNIST-Width-Solution.ipynb 14.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.3 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - TensorFlow-Minimal-Example-All-Exercises.ipynb 14.3 kB35 - Advanced Statistical Methods Practical Example Linear Regression/231 - Practical Example Linear Regression Part 5 English.srt 14.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.3 kB51 - Deep Learning Business Case Example/351 - Business Case Exploring the Dataset and Identifying Predictors English.srt 14.2 kB2 - The Field of Data Science The Various Data Science Disciplines/6 - Business Analytics Data Analytics and Data Science An Introduction English.srt 14.2 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/390 - Business Case Getting Acquainted with the Dataset English.srt 14.1 kB18 - Statistics Inferential Statistics Confidence Intervals/112 - 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 14.1 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Basic NN Example Part 4 English.srt 14.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/216 - sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 14.0 kB5 - The Field of Data Science Popular Data Science Techniques/11 - Techniques for Working with Traditional Data English.srt 14.0 kB56 - Software Integration/404 - Taking a Closer Look at APIs English.srt 13.9 kB63 - Appendix pandas Fundamentals/475 - Introduction to pandas Series English.srt 13.9 kB63 - Appendix pandas Fundamentals/475 - Location.csv 13.8 kB63 - Appendix pandas Fundamentals/487 - Location.csv 13.8 kB5 - The Field of Data Science Popular Data Science Techniques/20 - Types of Machine Learning English.srt 13.8 kB62 - Appendix Additional Python Tools/469 - Additional-Python-Tools-Lectures.ipynb 13.8 kB62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Lectures.ipynb 13.8 kB64 - Appendix Working with Text Files in Python/515 - Saving-Data-NP-Solution.ipynb 13.7 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - Multiple-Linear-Regression-Exercise-Solution.ipynb 13.7 kB58 - Case Study Preprocessing the Absenteeismdata/425 - Classifying the Various Reasons for Absence English.srt 13.5 kB63 - Appendix pandas Fundamentals/485 - Data Selection in pandas DataFrames English.srt 13.5 kB15 - Statistics Descriptive Statistics/76 - 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.5 kB58 - Case Study Preprocessing the Absenteeismdata/420 - Obtaining Dummies from a Single Feature English.srt 13.5 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - MNIST Learning English.srt 13.4 kB62 - Appendix Additional Python Tools/474 - Anonymous Lambda Functions English.srt 13.4 kB12 - Probability Distributions/53 - Types of Probability Distributions English.srt 13.4 kB28 - Python Sequences/166 - Lists English.srt 13.4 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - 12.9.TensorFlow-MNIST-with-comments.ipynb 13.3 kB34 - Advanced Statistical Methods Linear Regression with sklearn/214 - sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.3 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Minimal-example-All-Exercises.ipynb 13.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/218 - SKLEAR-1.IPY 13.2 kB20 - Statistics Hypothesis Testing/130 - 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 13.1 kB61 - Case Study Analyzing the Predicted Outputs in Tableau/464 - Analyzing Age vs Probability in Tableau English.srt 13.1 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/398 - TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB34 - Advanced Statistical Methods Linear Regression with sklearn/215 - sklearn-How-to-properly-include-p-values.ipynb 13.0 kB13 - Probability Probability in Other Fields/67 - Probability in Finance English.srt 12.9 kB20 - Statistics Hypothesis Testing/128 - 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.9 kB15 - Statistics Descriptive Statistics/88 - 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.9 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dealing with Categorical Data Dummy Variables English.srt 12.9 kB50 - Deep Learning Classifying on the MNIST Dataset/348 - TensorFlow-MNIST-Part6-with-comments.ipynb 12.8 kB38 - Advanced Statistical Methods KMeans Clustering/254 - A Simple Example of Clustering English.srt 12.6 kB34 - Advanced Statistical Methods Linear Regression with sklearn/223 - Train Test Split Explained English.srt 12.5 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - 5.6.TensorFlow-Minimal-example-complete.ipynb 12.4 kB64 - Appendix Working with Text Files in Python/503 - Importing Data Partial Cleaning While Importing Data English.srt 12.4 kB18 - Statistics Inferential Statistics Confidence Intervals/104 - Confidence Intervals Population Variance Known Zscore English.srt 12.3 kB17 - Statistics Inferential Statistics Fundamentals/99 - 3.4.Standard-normal-distribution-exercise.xlsx 12.3 kB51 - Deep Learning Business Case Example/361 - TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB51 - Deep Learning Business Case Example/362 - TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB61 - Case Study Analyzing the Predicted Outputs in Tableau/466 - Analyzing Reasons vs Probability in Tableau English.srt 12.2 kB50 - Deep Learning Classifying on the MNIST Dataset/344 - MNIST Preprocess the Data Shuffle and Batch English.srt 12.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/218 - sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 12.0 kB5 - The Field of Data Science Popular Data Science Techniques/19 - Machine Learning ML Techniques English.srt 12.0 kB36 - Advanced Statistical Methods Logistic Regression/244 - Accuracy-with-comments.ipynb 12.0 kB15 - Statistics Descriptive Statistics/88 - 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.9 kB64 - Appendix Working with Text Files in Python/502 - Importing-Text-Data-with-NumPy-Complete.ipynb 11.8 kB22 - Part 4 Introduction to Python/140 - Installing Python and Jupyter English.srt 11.8 kB34 - Advanced Statistical Methods Linear Regression with sklearn/218 - Feature Scaling Standardization English.srt 11.8 kB64 - Appendix Working with Text Files in Python/500 - Importing csv Files Part III English.srt 11.8 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - MNIST Model Outline English.srt 11.8 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/386 - 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.8 kB3 - The Field of Data Science Connecting the Data Science Disciplines/9 - Applying Traditional Data Big Data BI Traditional Data Science and ML English.srt 11.7 kB15 - Statistics Descriptive Statistics/75 - 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.7 kB40 - Part 6 Mathematics/280 - Dot Product of Matrices English.srt 11.7 kB12 - Probability Distributions/59 - Characteristics of Continuous Distributions English.srt 11.7 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/298 - Minimal-example-Part-4-Complete.ipynb 11.7 kB56 - Software Integration/403 - What are Data Connectivity APIs and Endpoints English.srt 11.7 kB38 - Advanced Statistical Methods KMeans Clustering/264 - Market Segmentation with Cluster Analysis Part 2 English.srt 11.7 kB20 - Statistics Hypothesis Testing/134 - 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.7 kB13 - Probability Probability in Other Fields/68 - Probability in Statistics English.srt 11.7 kB62 - Appendix Additional Python Tools/469 - Additional-Python-Tools-Exercises.ipynb 11.6 kB62 - Appendix Additional Python Tools/474 - Additional-Python-Tools-Exercises.ipynb 11.6 kB15 - Statistics Descriptive Statistics/82 - 2.7.Mean-median-and-mode-exercise-solution.xlsx 11.6 kB20 - Statistics Hypothesis Testing/128 - 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.6 kB20 - Statistics Hypothesis Testing/132 - 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.5 kB20 - Statistics Hypothesis Testing/125 - 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.5 kB18 - Statistics Inferential Statistics Confidence Intervals/104 - 3.9.Population-variance-known-z-score-lesson.xlsx 11.5 kB51 - Deep Learning Business Case Example/354 - TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB18 - Statistics Inferential Statistics Confidence Intervals/105 - 3.9.Population-variance-known-z-score-exercise-solution.xlsx 11.4 kB28 - Python Sequences/169 - Dictionaries English.srt 11.4 kB18 - Statistics Inferential Statistics Confidence Intervals/109 - 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.4 kB9 - Part 2 Probability/25 - The Basic Probability Formula English.srt 11.3 kB15 - Statistics Descriptive Statistics/86 - 2.9.Variance-exercise-solution.xlsx 11.3 kB64 - Appendix Working with Text Files in Python/498 - Importing csv Files Part I English.srt 11.3 kB58 - Case Study Preprocessing the Absenteeismdata/435 - Analyzing the Dates from the Initial Data Set English.srt 11.3 kB20 - Statistics Hypothesis Testing/125 - 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.3 kB50 - Deep Learning Classifying on the MNIST Dataset/347 - TensorFlow-MNIST-Part5-with-comments.ipynb 11.2 kB42 - Deep Learning Introduction to Neural Networks/293 - Optimization Algorithm 1Parameter Gradient Descent English.srt 11.2 kB15 - Statistics Descriptive Statistics/87 - 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.2 kB20 - Statistics Hypothesis Testing/124 - 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.2 kB12 - Probability Distributions/57 - Discrete Distributions The Binomial Distribution English.srt 11.2 kB5 - The Field of Data Science Popular Data Science Techniques/15 - Business Intelligence BI Techniques English.srt 11.2 kB15 - Statistics Descriptive Statistics/82 - 2.7.Mean-median-and-mode-exercise.xlsx 11.1 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/444 - Creating the Targets for the Logistic Regression English.srt 11.1 kB18 - Statistics Inferential Statistics Confidence Intervals/105 - 3.9.Population-variance-known-z-score-exercise.xlsx 11.1 kB15 - Statistics Descriptive Statistics/86 - 2.9.Variance-exercise.xlsx 11.1 kB21 - Statistics Practical Example Hypothesis Testing/135 - Practical Example Hypothesis Testing English.srt 11.0 kB18 - Statistics Inferential Statistics Confidence Intervals/108 - 3.11.Population-variance-unknown-t-score-lesson.xlsx 11.0 kB29 - Python Iterations/172 - Lists with the range Function English.srt 11.0 kB20 - Statistics Hypothesis Testing/132 - 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11.0 kB38 - Advanced Statistical Methods KMeans Clustering/267 - Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 11.0 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/447 - Splitting the Data for Training and Testing English.srt 11.0 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/450 - Interpreting the Coefficients for Our Problem English.srt 11.0 kB20 - Statistics Hypothesis Testing/122 - Rejection Region and Significance Level English.srt 11.0 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/398 - TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB18 - Statistics Inferential Statistics Confidence Intervals/109 - 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.9 kB62 - Appendix Additional Python Tools/472 - Triple Nested For Loops English.srt 10.9 kB62 - Appendix Additional Python Tools/471 - Introduction to Nested For Loops English.srt 10.9 kB44 - Deep Learning TensorFlow 20 Introduction/305 - Outlining the Model with TensorFlow 2 English.srt 10.8 kB18 - Statistics Inferential Statistics Confidence Intervals/111 - Confidence intervals Two means Dependent samples English.srt 10.8 kB20 - Statistics Hypothesis Testing/134 - 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.8 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/181 - The Linear Regression Model English.srt 10.8 kB15 - Statistics Descriptive Statistics/81 - 2.7.Mean-median-and-mode-lesson.xlsx 10.7 kB50 - Deep Learning Classifying on the MNIST Dataset/346 - TensorFlow-MNIST-Part4-with-comments.ipynb 10.7 kB12 - Probability Distributions/52 - Fundamentals of Probability Distributions English.srt 10.7 kB18 - Statistics Inferential Statistics Confidence Intervals/111 - 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.7 kB34 - Advanced Statistical Methods Linear Regression with sklearn/214 - sklearn-Feature-Selection-with-F-regression.ipynb 10.7 kB34 - Advanced Statistical Methods Linear Regression with sklearn/212 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.7 kB63 - Appendix pandas Fundamentals/486 - pandas DataFrames Indexing with iloc English.srt 10.7 kB35 - Advanced Statistical Methods Practical Example Linear Regression/225 - Practical Example Linear Regression Part 2 English.srt 10.6 kB17 - Statistics Inferential Statistics Fundamentals/98 - 3.4.Standard-normal-distribution-lesson.xlsx 10.6 kB58 - Case Study Preprocessing the Absenteeismdata/416 - Dropping a Column from a DataFrame in Python English.srt 10.6 kB64 - Appendix Working with Text Files in Python/508 - Importing Data in Python an Important Exercise English.srt 10.6 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - First Regression in Python English.srt 10.6 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.6 kB38 - Advanced Statistical Methods KMeans Clustering/257 - Categorical.csv 10.6 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/395 - Creating a Data Provider English.srt 10.6 kB34 - Advanced Statistical Methods Linear Regression with sklearn/213 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.6 kB15 - Statistics Descriptive Statistics/85 - Variance English.srt 10.5 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/387 - MNIST Results and Testing English.srt 10.5 kB63 - Appendix pandas Fundamentals/475 - Region.csv 10.5 kB63 - Appendix pandas Fundamentals/487 - Region.csv 10.5 kB20 - Statistics Hypothesis Testing/124 - Test for the Mean Population Variance Known English.srt 10.4 kB18 - Statistics Inferential Statistics Confidence Intervals/114 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.4 kB51 - Deep Learning Business Case Example/359 - Business Case Setting an Early Stopping Mechanism English.srt 10.3 kB15 - Statistics Descriptive Statistics/85 - 2.9.Variance-lesson.xlsx 10.3 kB51 - Deep Learning Business Case Example/359 - TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.3 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - Basic NN Example with TF Inputs Outputs Targets Weights Biases English.srt 10.3 kB51 - Deep Learning Business Case Example/355 - TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB6 - The Field of Data Science Popular Data Science Tools/22 - Necessary Programming Languages and Software Used in Data Science English.srt 10.3 kB60 - Case Study Loading the absenteeismmodule/461 - Deploying the absenteeismmodule Part II English.srt 10.3 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB63 - Appendix pandas Fundamentals/483 - Introduction to pandas DataFrames Part II English.srt 10.2 kB58 - Case Study Preprocessing the Absenteeismdata/436 - Extracting the Month Value from the Date Column English.srt 10.2 kB50 - Deep Learning Classifying on the MNIST Dataset/348 - MNIST Learning English.srt 10.2 kB29 - Python Iterations/173 - Conditional Statements and Loops English.srt 10.2 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/377 - Basic NN Example with TF Model Output English.srt 10.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/213 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 10.1 kB64 - Appendix Working with Text Files in Python/512 - Saving-Data-NP-Complete.ipynb 10.1 kB18 - Statistics Inferential Statistics Confidence Intervals/113 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 10.1 kB18 - Statistics Inferential Statistics Confidence Intervals/114 - 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 10.1 kB22 - Part 4 Introduction to Python/142 - Prerequisites for Coding in the Jupyter Notebooks English.srt 10.0 kB18 - Statistics Inferential Statistics Confidence Intervals/116 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 10.0 kB20 - Statistics Hypothesis Testing/129 - 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB29 - Python Iterations/175 - How to Iterate over Dictionaries English.srt 10.0 kB12 - Probability Distributions/66 - Customers-Membership.xlsx 9.9 kB42 - Deep Learning Introduction to Neural Networks/294 - Optimization Algorithm nParameter Gradient Descent English.srt 9.9 kB23 - Python Variables and Data Types/145 - Python Strings English.srt 9.9 kB20 - Statistics Hypothesis Testing/131 - 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB34 - Advanced Statistical Methods Linear Regression with sklearn/219 - Feature Selection through Standardization of Weights English.srt 9.9 kB61 - Case Study Analyzing the Predicted Outputs in Tableau/468 - Analyzing Transportation Expense vs Probability in Tableau English.srt 9.8 kB11 - Probability Bayesian Inference/50 - Bayes Law English.srt 9.8 kB12 - Probability Distributions/66 - Daily-Views.xlsx 9.8 kB18 - Statistics Inferential Statistics Confidence Intervals/115 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB15 - Statistics Descriptive Statistics/84 - 2.8.Skewness-exercise.xlsx 9.7 kB39 - Advanced Statistical Methods Other Types of Clustering/269 - Dendrogram English.srt 9.7 kB34 - Advanced Statistical Methods Linear Regression with sklearn/207 - Simple Linear Regression with sklearn English.srt 9.7 kB64 - Appendix Working with Text Files in Python/512 - Saving Your Data with NumPy Part I npy English.srt 9.7 kB38 - Advanced Statistical Methods KMeans Clustering/258 - How to Choose the Number of Clusters English.srt 9.7 kB64 - Appendix Working with Text Files in Python/505 - Importing Data from json Files English.srt 9.6 kB38 - Advanced Statistical Methods KMeans Clustering/263 - Market Segmentation with Cluster Analysis Part 1 English.srt 9.6 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making-predictions-with-comments.ipynb 9.6 kB64 - Appendix Working with Text Files in Python/492 - Importing Data in Python Principles English.srt 9.6 kB28 - Python Sequences/168 - Tuples English.srt 9.6 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Adjusted RSquared English.srt 9.6 kB2 - The Field of Data Science The Various Data Science Disciplines/4 - Data Science and Business Buzzwords Why are there so Many English.srt 9.6 kB20 - Statistics Hypothesis Testing/133 - 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/452 - Interpreting the Coefficients of the Logistic Regression English.srt 9.5 kB64 - Appendix Working with Text Files in Python/497 - Importing Text Files with open English.srt 9.5 kB18 - Statistics Inferential Statistics Confidence Intervals/116 - 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB63 - Appendix pandas Fundamentals/482 - Introduction to pandas DataFrames Part I English.srt 9.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/212 - sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/448 - Fitting the Model and Assessing its Accuracy English.srt 9.3 kB44 - Deep Learning TensorFlow 20 Introduction/305 - TensorFlow-Minimal-example-Part2.ipynb 9.3 kB34 - Advanced Statistical Methods Linear Regression with sklearn/223 - sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB22 - Part 4 Introduction to Python/137 - Introduction to Programming English.srt 9.3 kB12 - Probability Distributions/58 - Discrete Distributions The Poisson Distribution English.srt 9.2 kB63 - Appendix pandas Fundamentals/477 - Working with Methods in Python Part I English.srt 9.2 kB22 - Part 4 Introduction to Python/138 - Why Python English.srt 9.2 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/396 - Business Case Model Outline English.srt 9.2 kB50 - Deep Learning Classifying on the MNIST Dataset/346 - MNIST Outline the Model English.srt 9.1 kB20 - Statistics Hypothesis Testing/120 - Null vs Alternative Hypothesis English.srt 9.1 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/199 - A3 Normality and Homoscedasticity English.srt 9.1 kB13 - Probability Probability in Other Fields/69 - Probability in Data Science English.srt 9.1 kB58 - Case Study Preprocessing the Absenteeismdata/412 - Checking the Content of the Data Set English.srt 9.1 kB9 - Part 2 Probability/28 - Events and Their Complements English.srt 9.1 kB30 - Python Advanced Python Tools/176 - Object Oriented Programming English.srt 9.0 kB34 - Advanced Statistical Methods Linear Regression with sklearn/214 - Feature Selection Fregression English.srt 8.9 kB9 - Part 2 Probability/27 - Frequency English.srt 8.9 kB9 - Part 2 Probability/26 - Computing Expected Values English.srt 8.9 kB34 - Advanced Statistical Methods Linear Regression with sklearn/211 - sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/397 - Business Case Optimization English.srt 8.9 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB56 - Software Integration/406 - Software Integration Explained English.srt 8.8 kB46 - Deep Learning Overfitting/323 - Early Stopping or When to Stop Training English.srt 8.8 kB50 - Deep Learning Classifying on the MNIST Dataset/345 - TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB51 - Deep Learning Business Case Example/355 - TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/311 - Digging into a Deep Net English.srt 8.8 kB15 - Statistics Descriptive Statistics/79 - Cross Tables and Scatter Plots English.srt 8.8 kB64 - Appendix Working with Text Files in Python/493 - Plain Text Files Flat Files and More English.srt 8.8 kB26 - Python Conditional Statements/157 - The ELIF Statement English.srt 8.7 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/385 - 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB58 - Case Study Preprocessing the Absenteeismdata/441 - Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB44 - Deep Learning TensorFlow 20 Introduction/306 - Interpreting the Result and Extracting the Weights and Bias English.srt 8.7 kB38 - Advanced Statistical Methods KMeans Clustering/259 - How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB1 - Part 1 Introduction/1 - A Practical Example What You Will Learn in This Course English.srt 8.7 kB34 - Advanced Statistical Methods Linear Regression with sklearn/208 - Simple Linear Regression with sklearn A StatsModelslike Summary Table English.srt 8.7 kB20 - Statistics Hypothesis Testing/129 - Test for the Mean Dependent Samples English.srt 8.7 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/191 - RSquared English.srt 8.6 kB38 - Advanced Statistical Methods KMeans Clustering/265 - How is Clustering Useful English.srt 8.6 kB29 - Python Iterations/170 - For Loops English.srt 8.6 kB58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/296 - Basic NN Example Part 2 English.srt 8.5 kB15 - Statistics Descriptive Statistics/73 - Categorical Variables Visualization Techniques English.srt 8.5 kB36 - Advanced Statistical Methods Logistic Regression/248 - Bank-data-testing.csv 8.5 kB64 - Appendix Working with Text Files in Python/513 - Saving Your Data with NumPy Part II npz English.srt 8.5 kB38 - Advanced Statistical Methods KMeans Clustering/255 - Countries-exercise.csv 8.5 kB38 - Advanced Statistical Methods KMeans Clustering/259 - Countries-exercise.csv 8.5 kB34 - Advanced Statistical Methods Linear Regression with sklearn/212 - Calculating the Adjusted RSquared in sklearn English.srt 8.5 kB38 - Advanced Statistical Methods KMeans Clustering/253 - KMeans Clustering English.srt 8.4 kB44 - Deep Learning TensorFlow 20 Introduction/300 - How to Install TensorFlow 20 English.srt 8.4 kB36 - Advanced Statistical Methods Logistic Regression/247 - Testing the Model English.srt 8.3 kB63 - Appendix pandas Fundamentals/484 - pandas DataFrames Common Attributes English.srt 8.3 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/454 - Testing the Model We Created English.srt 8.3 kB50 - Deep Learning Classifying on the MNIST Dataset/342 - MNIST Preprocess the Data Create a Validation Set and Scale It English.srt 8.3 kB62 - Appendix Additional Python Tools/470 - Iterating Over Range Objects English.srt 8.3 kB4 - The Field of Data Science The Benefits of Each Discipline/10 - The Reason Behind These Disciplines English.srt 8.3 kB18 - Statistics Inferential Statistics Confidence Intervals/110 - Margin of Error English.srt 8.2 kB51 - Deep Learning Business Case Example/358 - Business Case Learning and Interpreting the Result English.srt 8.2 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/188 - How to Interpret the Regression Table English.srt 8.1 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/330 - Learning Rate Schedules or How to Choose the Optimal Learning Rate English.srt 8.1 kB15 - Statistics Descriptive Statistics/87 - Standard Deviation and Coefficient of Variation English.srt 8.1 kB38 - Advanced Statistical Methods KMeans Clustering/261 - To Standardize or not to Standardize English.srt 8.0 kB56 - Software Integration/402 - What are Data Servers Clients Requests and Responses English.srt 8.0 kB18 - Statistics Inferential Statistics Confidence Intervals/113 - Confidence intervals Two means Independent Samples Part 1 English.srt 8.0 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/449 - Creating a Summary Table with the Coefficients and Intercept English.srt 8.0 kB52 - Deep Learning Conclusion/366 - An overview of CNNs English.srt 8.0 kB34 - Advanced Statistical Methods Linear Regression with sklearn/211 - sklearn-Multiple-Linear-Regression.ipynb 8.0 kB37 - Advanced Statistical Methods Cluster Analysis/250 - Some Examples of Clusters English.srt 8.0 kB42 - Deep Learning Introduction to Neural Networks/283 - Introduction to Neural Networks English.srt 8.0 kB11 - Probability Bayesian Inference/43 - Union of Sets English.srt 7.9 kB40 - Part 6 Mathematics/274 - Arrays in Python A Convenient Way To Represent Matrices English.srt 7.9 kB49 - Deep Learning Preprocessing/336 - Standardization English.srt 7.9 kB39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps English.srt 7.9 kB29 - Python Iterations/171 - While Loops and Incrementing English.srt 7.8 kB36 - Advanced Statistical Methods Logistic Regression/247 - Testing-the-model-with-comments.ipynb 7.7 kB23 - Python Variables and Data Types/145 - Strings-Lecture-Py3.ipynb 7.7 kB25 - Python Other Python Operators/154 - Logical and Identity Operators English.srt 7.7 kB10 - Probability Combinatorics/34 - Solving Combinations English.srt 7.7 kB38 - Advanced Statistical Methods KMeans Clustering/258 - Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB58 - Case Study Preprocessing the Absenteeismdata/419 - Analyzing the Reasons for Absence English.srt 7.7 kB58 - Case Study Preprocessing the Absenteeismdata/440 - Working on Education Children and Pets English.srt 7.7 kB15 - Statistics Descriptive Statistics/81 - Mean median and mode English.srt 7.6 kB64 - Appendix Working with Text Files in Python/509 - Customer-Gender.csv 7.6 kB11 - Probability Bayesian Inference/46 - The Conditional Probability Formula English.srt 7.6 kB20 - Statistics Hypothesis Testing/127 - Test for the Mean Population Variance Unknown English.srt 7.6 kB50 - Deep Learning Classifying on the MNIST Dataset/350 - MNIST Testing the Model English.srt 7.6 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/458 - Preparing the Deployment of the Model through a Module English.srt 7.6 kB64 - Appendix Working with Text Files in Python/488 - An Introduction to Working with Files in Python English.srt 7.6 kB38 - Advanced Statistical Methods KMeans Clustering/266 - Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB36 - Advanced Statistical Methods Logistic Regression/234 - A Simple Example in Python English.srt 7.5 kB17 - Statistics Inferential Statistics Fundamentals/96 - What is a Distribution English.srt 7.5 kB58 - Case Study Preprocessing the Absenteeismdata/438 - Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB64 - Appendix Working with Text Files in Python/495 - Common Naming Conventions English.srt 7.5 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/383 - 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB56 - Software Integration/405 - Communication between Software Products through Text Files English.srt 7.5 kB14 - Part 3 Statistics/70 - Population and Sample English.srt 7.5 kB63 - Appendix pandas Fundamentals/480 - Using unique and nunique English.srt 7.4 kB5 - The Field of Data Science Popular Data Science Techniques/13 - Techniques for Working with Big Data English.srt 7.4 kB46 - Deep Learning Overfitting/318 - What is Overfitting English.srt 7.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/223 - sklearn-Train-Test-Split.ipynb 7.4 kB15 - Statistics Descriptive Statistics/71 - Types of Data English.srt 7.4 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/455 - Saving the Model and Preparing it for Deployment English.srt 7.3 kB52 - Deep Learning Conclusion/368 - An Overview of nonNN Approaches English.srt 7.3 kB63 - Appendix pandas Fundamentals/479 - Parameters and Arguments in pandas English.srt 7.3 kB12 - Probability Distributions/61 - Continuous Distributions The Standard Normal Distribution English.srt 7.3 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dummy-variables-with-comments.ipynb 7.3 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/198 - A2 No Endogeneity English.srt 7.2 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/332 - Adaptive Learning Rate Schedules AdaGrad and RMSprop English.srt 7.2 kB18 - Statistics Inferential Statistics Confidence Intervals/106 - Confidence Interval Clarifications English.srt 7.1 kB36 - Advanced Statistical Methods Logistic Regression/239 - Understanding Logistic Regression Tables English.srt 7.1 kB40 - Part 6 Mathematics/278 - Transpose of a Matrix English.srt 7.1 kB17 - Statistics Inferential Statistics Fundamentals/100 - Central Limit Theorem English.srt 7.1 kB63 - Appendix pandas Fundamentals/487 - pandas DataFrames Indexing with loc English.srt 7.1 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/184 - Python Packages Installation English.srt 7.1 kB64 - Appendix Working with Text Files in Python/506 - An Introduction to Working with Excel Files in Python English.srt 7.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/220 - Predicting with the Standardized Coefficients English.srt 7.1 kB57 - Case Study Whats Next in the Course/407 - Game Plan for this Python SQL and Tableau Business Exercise English.srt 7.0 kB63 - Appendix pandas Fundamentals/481 - Using sortvalues English.srt 7.0 kB28 - Python Sequences/167 - List Slicing English.srt 7.0 kB20 - Statistics Hypothesis Testing/123 - Type I Error and Type II Error English.srt 7.0 kB44 - Deep Learning TensorFlow 20 Introduction/301 - TensorFlow Outline and Comparison with Other Libraries English.srt 7.0 kB38 - Advanced Statistical Methods KMeans Clustering/264 - Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB8 - The Field of Data Science Debunking Common Misconceptions/24 - Debunking Common Misconceptions English.srt 7.0 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Minimal-example-Part-3.ipynb 7.0 kB36 - Advanced Statistical Methods Logistic Regression/248 - Testing-the-Model-Exercise.ipynb 7.0 kB20 - Statistics Hypothesis Testing/131 - Test for the mean Independent Samples Part 1 English.srt 7.0 kB12 - Probability Distributions/56 - Discrete Distributions The Bernoulli Distribution English.srt 6.9 kB42 - Deep Learning Introduction to Neural Networks/285 - Types of Machine Learning English.srt 6.9 kB50 - Deep Learning Classifying on the MNIST Dataset/350 - TensorFlow-MNIST-complete.ipynb 6.9 kB1 - Part 1 Introduction/2 - What Does the Course Cover English.srt 6.9 kB49 - Deep Learning Preprocessing/338 - Binary and OneHot Encoding English.srt 6.9 kB11 - Probability Bayesian Inference/40 - Sets and Events English.srt 6.9 kB52 - Deep Learning Conclusion/363 - Summary on What Youve Learned English.srt 6.9 kB64 - Appendix Working with Text Files in Python/514 - Saving Your Data with NumPy Part III csv English.srt 6.9 kB20 - Statistics Hypothesis Testing/133 - Test for the mean Independent Samples Part 2 English.srt 6.9 kB42 - Deep Learning Introduction to Neural Networks/292 - Common Objective Functions CrossEntropy Loss English.srt 6.9 kB18 - Statistics Inferential Statistics Confidence Intervals/108 - Confidence Intervals Population Variance Unknown Tscore English.srt 6.9 kB12 - Probability Distributions/65 - Continuous Distributions The Logistic Distribution English.srt 6.9 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/400 - Business Case A Comment on the Homework English.srt 6.8 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Backward Elimination or How to Simplify Your Model English.srt 6.8 kB60 - Case Study Loading the absenteeismmodule/459 - absenteeism-module.py 6.8 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/384 - Calculating the Accuracy of the Model English.srt 6.7 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/372 - TensorFlow Intro English.srt 6.7 kB20 - Statistics Hypothesis Testing/126 - pvalue English.srt 6.7 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/451 - Standardizing only the Numerical Variables Creating a Custom Scaler English.srt 6.7 kB36 - Advanced Statistical Methods Logistic Regression/242 - Binary Predictors in a Logistic Regression English.srt 6.7 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/313 - Activation Functions English.srt 6.7 kB17 - Statistics Inferential Statistics Fundamentals/97 - The Normal Distribution English.srt 6.6 kB58 - Case Study Preprocessing the Absenteeismdata/426 - Using concat in Python English.srt 6.6 kB36 - Advanced Statistical Methods Logistic Regression/246 - Underfitting and Overfitting English.srt 6.6 kB50 - Deep Learning Classifying on the MNIST Dataset/343 - TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB15 - Statistics Descriptive Statistics/89 - Covariance English.srt 6.5 kB2 - The Field of Data Science The Various Data Science Disciplines/5 - What is the difference between Analysis and Analytics English.srt 6.5 kB12 - Probability Distributions/60 - Continuous Distributions The Normal Distribution English.srt 6.5 kB39 - Advanced Statistical Methods Other Types of Clustering/268 - Types of Clustering English.srt 6.5 kB2 - The Field of Data Science The Various Data Science Disciplines/8 - A Breakdown of our Data Science Infographic English.srt 6.4 kB36 - Advanced Statistical Methods Logistic Regression/235 - Logistic vs Logit Function English.srt 6.4 kB64 - Appendix Working with Text Files in Python/490 - Structured SemiStructured and Unstructured Data English.srt 6.4 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/200 - A4 No Autocorrelation English.srt 6.4 kB36 - Advanced Statistical Methods Logistic Regression/237 - Example-bank-data.csv 6.4 kB46 - Deep Learning Overfitting/320 - What is Validation English.srt 6.3 kB60 - Case Study Loading the absenteeismmodule/460 - Deploying the absenteeismmodule Part I English.srt 6.3 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/375 - 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB15 - Statistics Descriptive Statistics/91 - Correlation Coefficient English.srt 6.3 kB10 - Probability Combinatorics/33 - Solving Variations without Repetition English.srt 6.3 kB28 - Python Sequences/169 - Dictionaries-Solution-Py3.ipynb 6.3 kB37 - Advanced Statistical Methods Cluster Analysis/249 - Introduction to Cluster Analysis English.srt 6.3 kB22 - Part 4 Introduction to Python/139 - Why Jupyter English.srt 6.3 kB30 - Python Advanced Python Tools/179 - Importing Modules in Python English.srt 6.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/382 - 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/221 - sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/376 - Basic NN Example with TF Loss Function and Gradient Descent English.srt 6.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/207 - sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB41 - Part 7 Deep Learning/282 - What to Expect from this Part English.srt 6.2 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/327 - Stochastic Gradient Descent English.srt 6.2 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/443 - Exploring the Problem with a Machine Learning Mindset English.srt 6.2 kB64 - Appendix Working with Text Files in Python/510 - Importing Files in Jupyter English.srt 6.2 kB58 - Case Study Preprocessing the Absenteeismdata/437 - Extracting the Day of the Week from the Date Column English.srt 6.2 kB23 - Python Variables and Data Types/143 - Variables English.srt 6.2 kB64 - Appendix Working with Text Files in Python/511 - Saving Your Data with pandas English.srt 6.2 kB64 - Appendix Working with Text Files in Python/515 - Saving-Data-NP-Exercise.ipynb 6.1 kB42 - Deep Learning Introduction to Neural Networks/288 - The Linear model with Multiple Inputs and Multiple Outputs English.srt 6.1 kB15 - Statistics Descriptive Statistics/72 - Levels of Measurement English.srt 6.1 kB42 - Deep Learning Introduction to Neural Networks/284 - Training the Model English.srt 6.1 kB38 - Advanced Statistical Methods KMeans Clustering/263 - Market-segmentation-example-with-comments.ipynb 6.0 kB64 - Appendix Working with Text Files in Python/491 - Text Files and Data Connectivity English.srt 6.0 kB25 - Python Other Python Operators/154 - Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB11 - Probability Bayesian Inference/49 - The Multiplication Law English.srt 6.0 kB7 - The Field of Data Science Careers in Data Science/23 - Finding the Job What to Expect and What to Look for English.srt 6.0 kB18 - Statistics Inferential Statistics Confidence Intervals/115 - Confidence intervals Two means Independent Samples Part 2 English.srt 6.0 kB38 - Advanced Statistical Methods KMeans Clustering/254 - Country-clusters-with-comments.ipynb 5.9 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making-predictions.ipynb 5.9 kB36 - Advanced Statistical Methods Logistic Regression/247 - Testing-the-model.ipynb 5.9 kB51 - Deep Learning Business Case Example/356 - Business Case Load the Preprocessed Data English.srt 5.9 kB40 - Part 6 Mathematics/271 - What is a Matrix English.srt 5.9 kB64 - Appendix Working with Text Files in Python/509 - Importing Data with the squeeze Method English.srt 5.9 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/201 - A5 No Multicollinearity English.srt 5.8 kB58 - Case Study Preprocessing the Absenteeismdata/439 - Analyzing Several Straightforward Columns for this Exercise English.srt 5.8 kB38 - Advanced Statistical Methods KMeans Clustering/260 - Pros and Cons of KMeans Clustering English.srt 5.8 kB11 - Probability Bayesian Inference/41 - Ways Sets Can Interact English.srt 5.8 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/315 - Backpropagation English.srt 5.8 kB34 - Advanced Statistical Methods Linear Regression with sklearn/217 - sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB27 - Python Python Functions/160 - How to Create a Function with a Parameter English.srt 5.8 kB18 - Statistics Inferential Statistics Confidence Intervals/107 - Students T Distribution English.srt 5.8 kB38 - Advanced Statistical Methods KMeans Clustering/256 - Categorical-data-with-comments.ipynb 5.8 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Basic NN Example Part 1 English.srt 5.8 kB10 - Probability Combinatorics/35 - Symmetry of Combinations English.srt 5.7 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/189 - Decomposition of Variability English.srt 5.7 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/314 - Activation Functions Softmax Activation English.srt 5.7 kB51 - Deep Learning Business Case Example/354 - TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/393 - TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB35 - Advanced Statistical Methods Practical Example Linear Regression/227 - Practical Example Linear Regression Part 3 English.srt 5.7 kB12 - Probability Distributions/64 - Continuous Distributions The Exponential Distribution English.srt 5.7 kB37 - Advanced Statistical Methods Cluster Analysis/252 - Math Prerequisites English.srt 5.7 kB38 - Advanced Statistical Methods KMeans Clustering/259 - How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/204 - Making Predictions with the Linear Regression English.srt 5.7 kB27 - Python Python Functions/165 - Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB15 - Statistics Descriptive Statistics/75 - Numerical Variables Frequency Distribution Table English.srt 5.6 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/297 - Basic NN Example Part 3 English.srt 5.6 kB36 - Advanced Statistical Methods Logistic Regression/241 - What do the Odds Actually Mean English.srt 5.6 kB10 - Probability Combinatorics/30 - Permutations and How to Use Them English.srt 5.6 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/392 - The Importance of Working with a Balanced Dataset English.srt 5.6 kB23 - Python Variables and Data Types/145 - Strings-Solution-Py3.ipynb 5.6 kB24 - Python Basic Python Syntax/146 - Using Arithmetic Operators in Python English.srt 5.6 kB44 - Deep Learning TensorFlow 20 Introduction/307 - Customizing a TensorFlow 2 Model English.srt 5.5 kB40 - Part 6 Mathematics/279 - Dot Product English.srt 5.5 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/312 - NonLinearities and their Purpose English.srt 5.5 kB46 - Deep Learning Overfitting/322 - NFold Cross Validation English.srt 5.5 kB36 - Advanced Statistical Methods Logistic Regression/245 - Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB57 - Case Study Whats Next in the Course/409 - Introducing the Data Set English.srt 5.5 kB27 - Python Python Functions/165 - Builtin Functions in Python English.srt 5.5 kB51 - Deep Learning Business Case Example/353 - Business Case Balancing the Dataset English.srt 5.5 kB40 - Part 6 Mathematics/276 - Addition and Subtraction of Matrices English.srt 5.5 kB58 - Case Study Preprocessing the Absenteeismdata/413 - Introduction to Terms with Multiple Meanings English.srt 5.5 kB36 - Advanced Statistical Methods Logistic Regression/244 - Calculating the Accuracy of the Model English.srt 5.5 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/446 - Standardizing the Data English.srt 5.5 kB36 - Advanced Statistical Methods Logistic Regression/234 - Admittance-with-comments.ipynb 5.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/211 - Multiple Linear Regression with sklearn English.srt 5.4 kB64 - Appendix Working with Text Files in Python/489 - File vs File Object Reading vs Parsing Data English.srt 5.4 kB10 - Probability Combinatorics/37 - Combinatorics in RealLife The Lottery English.srt 5.4 kB40 - Part 6 Mathematics/273 - Linear Algebra and Geometry English.srt 5.3 kB57 - Case Study Whats Next in the Course/408 - The Business Task English.srt 5.2 kB49 - Deep Learning Preprocessing/334 - Preprocessing Introduction English.srt 5.2 kB10 - Probability Combinatorics/36 - Solving Combinations with Separate Sample Spaces English.srt 5.2 kB44 - Deep Learning TensorFlow 20 Introduction/302 - TensorFlow 1 vs TensorFlow 2 English.srt 5.2 kB58 - Case Study Preprocessing the Absenteeismdata/411 - Importing the Absenteeism Data in Python English.srt 5.2 kB40 - Part 6 Mathematics/272 - Scalars and Vectors English.srt 5.2 kB28 - Python Sequences/167 - List-Slicing-Lecture-Py3.ipynb 5.1 kB64 - Appendix Working with Text Files in Python/501 - Importing Data with indexcol English.srt 5.1 kB17 - Statistics Inferential Statistics Fundamentals/98 - The Standard Normal Distribution English.srt 5.1 kB17 - Statistics Inferential Statistics Fundamentals/102 - Estimators and Estimates English.srt 5.1 kB11 - Probability Bayesian Inference/47 - The Law of Total Probability English.srt 5.1 kB63 - Appendix pandas Fundamentals/478 - Working with Methods in Python Part II English.srt 5.0 kB34 - Advanced Statistical Methods Linear Regression with sklearn/207 - sklearn-Simple-Linear-Regression.ipynb 5.0 kB52 - Deep Learning Conclusion/367 - An Overview of RNNs English.srt 5.0 kB38 - Advanced Statistical Methods KMeans Clustering/257 - Clustering-Categorical-Data-Solution.ipynb 5.0 kB58 - Case Study Preprocessing the Absenteeismdata/432 - Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB30 - Python Advanced Python Tools/178 - What is the Standard Library English.srt 4.9 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/380 - MNIST How to Tackle the MNIST English.srt 4.9 kB36 - Advanced Statistical Methods Logistic Regression/240 - Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB10 - Probability Combinatorics/38 - A Recap of Combinatorics English.srt 4.9 kB23 - Python Variables and Data Types/144 - Numbers and Boolean Values in Python English.srt 4.9 kB64 - Appendix Working with Text Files in Python/499 - Importing csv Files Part II English.srt 4.9 kB40 - Part 6 Mathematics/275 - What is a Tensor English.srt 4.8 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/190 - What is the OLS English.srt 4.8 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/316 - Backpropagation Picture English.srt 4.8 kB47 - Deep Learning Initialization/326 - StateoftheArt Method Xavier Glorot Initialization English.srt 4.8 kB34 - Advanced Statistical Methods Linear Regression with sklearn/222 - Underfitting and Overfitting English.srt 4.8 kB38 - Advanced Statistical Methods KMeans Clustering/264 - Market-segmentation-example-Part2.ipynb 4.8 kB26 - Python Conditional Statements/155 - The IF Statement English.srt 4.8 kB47 - Deep Learning Initialization/325 - Types of Simple Initializations English.srt 4.8 kB10 - Probability Combinatorics/32 - Solving Variations with Repetition English.srt 4.8 kB38 - Advanced Statistical Methods KMeans Clustering/255 - A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB47 - Deep Learning Initialization/324 - What is Initialization English.srt 4.8 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - Dummy-Variables.ipynb 4.7 kB51 - Deep Learning Business Case Example/357 - TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB22 - Part 4 Introduction to Python/141 - Understanding Jupyters Interface the Notebook Dashboard English.srt 4.7 kB28 - Python Sequences/168 - Tuples-Solution-Py3.ipynb 4.7 kB15 - Statistics Descriptive Statistics/83 - Skewness English.srt 4.7 kB42 - Deep Learning Introduction to Neural Networks/286 - The Linear Model Linear Algebraic Version English.srt 4.7 kB40 - Part 6 Mathematics/274 - Scalars-Vectors-and-Matrices.ipynb 4.7 kB37 - Advanced Statistical Methods Cluster Analysis/251 - Difference between Classification and Clustering English.srt 4.7 kB38 - Advanced Statistical Methods KMeans Clustering/258 - Selecting-the-number-of-clusters.ipynb 4.6 kB34 - Advanced Statistical Methods Linear Regression with sklearn/205 - What is sklearn and How is it Different from Other Packages English.srt 4.6 kB50 - Deep Learning Classifying on the MNIST Dataset/339 - MNIST The Dataset English.srt 4.6 kB27 - Python Python Functions/165 - Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB36 - Advanced Statistical Methods Logistic Regression/243 - Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB50 - Deep Learning Classifying on the MNIST Dataset/340 - MNIST How to Tackle the MNIST English.srt 4.6 kB27 - Python Python Functions/163 - Conditional Statements and Functions English.srt 4.6 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/445 - Selecting the Inputs for the Logistic Regression English.srt 4.6 kB58 - Case Study Preprocessing the Absenteeismdata/432 - Creating Checkpoints while Coding in Jupyter English.srt 4.6 kB5 - The Field of Data Science Popular Data Science Techniques/18 - Real Life Examples of Traditional Methods English.srt 4.6 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/379 - MNIST What is the MNIST Dataset English.srt 4.6 kB38 - Advanced Statistical Methods KMeans Clustering/266 - Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB36 - Advanced Statistical Methods Logistic Regression/237 - Building-a-Logistic-Regression-Solution.ipynb 4.5 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/329 - Momentum English.srt 4.5 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/383 - MNIST Loss and Optimization Algorithm English.srt 4.5 kB36 - Advanced Statistical Methods Logistic Regression/236 - Building a Logistic Regression English.srt 4.5 kB28 - Python Sequences/169 - Dictionaries-Lecture-Py3.ipynb 4.5 kB65 - Bonus Lecture/517 - Bonus Lecture Next Steps.html 4.4 kB44 - Deep Learning TensorFlow 20 Introduction/304 - Types of File Formats Supporting TensorFlow English.srt 4.4 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/192 - Multiple Linear Regression English.srt 4.4 kB11 - Probability Bayesian Inference/45 - Dependence and Independence of Sets English.srt 4.4 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/374 - Types of File Formats supporting Tensors English.srt 4.4 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/333 - Adam Adaptive Moment Estimation English.srt 4.4 kB28 - Python Sequences/167 - List-Slicing-Solution-Py3.ipynb 4.4 kB46 - Deep Learning Overfitting/321 - Training Validation and Test Datasets English.srt 4.4 kB24 - Python Basic Python Syntax/146 - Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB10 - Probability Combinatorics/31 - Simple Operations with Factorials English.srt 4.3 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/370 - How to Install TensorFlow 1 English.srt 4.3 kB15 - Statistics Descriptive Statistics/77 - The Histogram English.srt 4.3 kB64 - Appendix Working with Text Files in Python/504 - Importing-Text-Data-DSc-Exercise.ipynb 4.3 kB38 - Advanced Statistical Methods KMeans Clustering/256 - Clustering Categorical Data English.srt 4.3 kB58 - Case Study Preprocessing the Absenteeismdata/441 - Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB36 - Advanced Statistical Methods Logistic Regression/236 - Admittance-regression-tables-fixed-error.ipynb 4.2 kB34 - Advanced Statistical Methods Linear Regression with sklearn/210 - Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.2 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - Simple-linear-regression-with-comments.ipynb 4.2 kB26 - Python Conditional Statements/156 - The ELSE Statement English.srt 4.1 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/310 - What is a Deep Net English.srt 4.1 kB18 - Statistics Inferential Statistics Confidence Intervals/103 - What are Confidence Intervals English.srt 4.1 kB12 - Probability Distributions/62 - Continuous Distributions The Students T Distribution English.srt 4.1 kB50 - Deep Learning Classifying on the MNIST Dataset/341 - TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB36 - Advanced Statistical Methods Logistic Regression/238 - An Invaluable Coding Tip English.srt 4.0 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/381 - 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB26 - Python Conditional Statements/158 - A Note on Boolean Values English.srt 4.0 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/398 - Business Case Interpretation English.srt 3.9 kB27 - Python Python Functions/161 - Defining a Function in Python Part II English.srt 3.9 kB34 - Advanced Statistical Methods Linear Regression with sklearn/216 - Creating a Summary Table with Pvalues English.srt 3.9 kB38 - Advanced Statistical Methods KMeans Clustering/263 - Market-segmentation-example.ipynb 3.9 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - Simple-linear-regression.ipynb 3.9 kB23 - Python Variables and Data Types/143 - Variables-Solution-Py3.ipynb 3.9 kB38 - Advanced Statistical Methods KMeans Clustering/257 - Clustering-Categorical-Data-Exercise.ipynb 3.9 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/196 - OLS Assumptions English.srt 3.9 kB50 - Deep Learning Classifying on the MNIST Dataset/341 - MNIST Importing the Relevant Packages and Loading the Data English.srt 3.9 kB34 - Advanced Statistical Methods Linear Regression with sklearn/206 - How are we Going to Approach this Section English.srt 3.8 kB50 - Deep Learning Classifying on the MNIST Dataset/347 - MNIST Select the Loss and the Optimizer English.srt 3.8 kB5 - The Field of Data Science Popular Data Science Techniques/21 - Real Life Examples of Machine Learning ML English.srt 3.8 kB58 - Case Study Preprocessing the Absenteeismdata/415 - Using a Statistical Approach towards the Solution to the Exercise English.srt 3.8 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/328 - Problems with Gradient Descent English.srt 3.8 kB12 - Probability Distributions/63 - Continuous Distributions The ChiSquared Distribution English.srt 3.8 kB27 - Python Python Functions/165 - Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/296 - Minimal-example-Part-2.ipynb 3.7 kB42 - Deep Learning Introduction to Neural Networks/291 - Common Objective Functions L2norm Loss English.srt 3.7 kB36 - Advanced Statistical Methods Logistic Regression/244 - Accuracy.ipynb 3.7 kB38 - Advanced Statistical Methods KMeans Clustering/267 - iris-with-answers.csv 3.7 kB38 - Advanced Statistical Methods KMeans Clustering/255 - A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB23 - Python Variables and Data Types/143 - Variables-Lecture-Py3.ipynb 3.7 kB40 - Part 6 Mathematics/280 - Dot-product-Part-2.ipynb 3.7 kB12 - Probability Distributions/55 - Discrete Distributions The Uniform Distribution English.srt 3.7 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/186 - Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB36 - Advanced Statistical Methods Logistic Regression/234 - Admittance.ipynb 3.6 kB46 - Deep Learning Overfitting/319 - Underfitting and Overfitting for Classification English.srt 3.6 kB24 - Python Basic Python Syntax/146 - Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB49 - Deep Learning Preprocessing/337 - Preprocessing Categorical Data English.srt 3.6 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/385 - MNIST Batching and Early Stopping English.srt 3.6 kB64 - Appendix Working with Text Files in Python/507 - Working with Excel xlsx Data English.srt 3.6 kB42 - Deep Learning Introduction to Neural Networks/287 - The Linear Model with Multiple Inputs English.srt 3.6 kB25 - Python Other Python Operators/154 - Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB11 - Probability Bayesian Inference/44 - Mutually Exclusive Sets English.srt 3.5 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/203 - real-estate-price-size-year-view.csv 3.5 kB23 - Python Variables and Data Types/144 - Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/374 - 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB38 - Advanced Statistical Methods KMeans Clustering/256 - Categorical-data.ipynb 3.4 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/399 - Business Case Testing the Model English.srt 3.4 kB42 - Deep Learning Introduction to Neural Networks/289 - Graphical Representation of Simple Neural Networks English.srt 3.4 kB40 - Part 6 Mathematics/277 - Errors when Adding Matrices English.srt 3.4 kB58 - Case Study Preprocessing the Absenteeismdata/441 - Final Remarks of this Section English.srt 3.4 kB45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/309 - What is a Layer English.srt 3.4 kB38 - Advanced Statistical Methods KMeans Clustering/254 - Country-clusters.ipynb 3.4 kB52 - Deep Learning Conclusion/364 - Whats Further out there in terms of Machine Learning English.srt 3.4 kB27 - Python Python Functions/161 - Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.4 kB27 - Python Python Functions/159 - Defining a Function in Python English.srt 3.4 kB55 - Appendix Deep Learning TensorFlow 1 Business Case/391 - Business Case Outlining the Solution English.srt 3.3 kB11 - Probability Bayesian Inference/48 - The Additive Rule English.srt 3.3 kB26 - Python Conditional Statements/157 - Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.3 kB25 - Python Other Python Operators/153 - Comparison Operators English.srt 3.3 kB23 - Python Variables and Data Types/144 - Numbers-and-Boolean-Values-Solution-Py3.ipynb 3.3 kB40 - Part 6 Mathematics/276 - Adding-and-subtracting-matrices.ipynb 3.3 kB28 - Python Sequences/166 - Lists-Solution-Py3.ipynb 3.3 kB11 - Probability Bayesian Inference/42 - Intersection of Sets English.srt 3.2 kB64 - Appendix Working with Text Files in Python/512 - Saving-Data-NP-Template.ipynb 3.2 kB40 - Part 6 Mathematics/277 - Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 kB36 - Advanced Statistical Methods Logistic Regression/240 - Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 kB12 - Probability Distributions/54 - Characteristics of Discrete Distributions English.srt 3.2 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/197 - A1 Linearity English.srt 3.2 kB29 - Python Iterations/174 - Conditional Statements Functions and Loops English.srt 3.2 kB24 - Python Basic Python Syntax/148 - Reassign-Values-Lecture-Py3.ipynb 3.2 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/195 - Test for Significance of the Model FTest English.srt 3.1 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/203 - Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.1 kB63 - Appendix pandas Fundamentals/476 - A Note on Completing the Upcoming Coding Exercises.html 3.0 kB29 - Python Iterations/173 - Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 kB28 - Python Sequences/169 - Dictionaries-Exercise-Py3.ipynb 3.0 kB36 - Advanced Statistical Methods Logistic Regression/237 - Building-a-Logistic-Regression-Exercise.ipynb 3.0 kB28 - Python Sequences/168 - Tuples-Lecture-Py3.ipynb 3.0 kB5 - The Field of Data Science Popular Data Science Techniques/12 - Real Life Examples of Traditional Data English.srt 3.0 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/373 - Actual Introduction to TensorFlow English.srt 3.0 kB31 - Part 5 Advanced Statistical Methods in Python/180 - Introduction to Regression Analysis English.srt 3.0 kB40 - Part 6 Mathematics/278 - Tranpose-of-a-matrix.ipynb 3.0 kB29 - Python Iterations/175 - Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 kB24 - Python Basic Python Syntax/152 - Structuring with Indentation English.srt 2.9 kB38 - Advanced Statistical Methods KMeans Clustering/262 - Relationship between Clustering and Regression English.srt 2.9 kB58 - Case Study Preprocessing the Absenteeismdata/414 - Whats Regression Analysis a Quick Refresher.html 2.9 kB64 - Appendix Working with Text Files in Python/494 - Text Files of Fixed Width English.srt 2.9 kB42 - Deep Learning Introduction to Neural Networks/290 - What is the Objective Function English.srt 2.9 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.9 kB28 - Python Sequences/167 - List-Slicing-Exercise-Py3.ipynb 2.9 kB48 - Deep Learning Digging into Gradient Descent and Learning Rate Schedules/331 - Learning Rate Schedules Visualized English.srt 2.8 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/186 - Simple-Linear-Regression-Exercise.ipynb 2.8 kB5 - The Field of Data Science Popular Data Science Techniques/16 - Real Life Examples of Business Intelligence BI English.srt 2.8 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/182 - Correlation vs Regression English.srt 2.8 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/381 - MNIST Relevant Packages English.srt 2.8 kB28 - Python Sequences/166 - Lists-Lecture-Py3.ipynb 2.8 kB51 - Deep Learning Business Case Example/361 - Business Case Testing the Model English.srt 2.7 kB24 - Python Basic Python Syntax/146 - Arithmetic-Operators-Exercise-Py3.ipynb 2.7 kB27 - Python Python Functions/162 - How to Use a Function within a Function English.srt 2.7 kB23 - Python Variables and Data Types/145 - Strings-Exercise-Py3.ipynb 2.7 kB17 - Statistics Inferential Statistics Fundamentals/101 - Standard error English.srt 2.7 kB36 - Advanced Statistical Methods Logistic Regression/242 - 2.02.Binary-predictors.csv 2.6 kB36 - Advanced Statistical Methods Logistic Regression/243 - Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.6 kB25 - Python Other Python Operators/153 - Comparison-Operators-Lecture-Py3.ipynb 2.6 kB18 - Statistics Inferential Statistics Confidence Intervals/117 - Confidence intervals Two means Independent Samples Part 3 English.srt 2.6 kB36 - Advanced Statistical Methods Logistic Regression/236 - Admittance-regression-summary-error.ipynb 2.5 kB58 - Case Study Preprocessing the Absenteeismdata/410 - What to Expect from the Following Sections.html 2.5 kB64 - Appendix Working with Text Files in Python/497 - Importing-Text-Files-in-Python-with-open.ipynb 2.5 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - Multiple-Linear-Regression-Exercise.ipynb 2.5 kB24 - Python Basic Python Syntax/149 - Add Comments English.srt 2.5 kB36 - Advanced Statistical Methods Logistic Regression/242 - Binary-predictors.ipynb 2.5 kB25 - Python Other Python Operators/153 - Comparison-Operators-Solution-Py3.ipynb 2.5 kB38 - Advanced Statistical Methods KMeans Clustering/266 - iris-dataset.csv 2.5 kB38 - Advanced Statistical Methods KMeans Clustering/267 - iris-dataset.csv 2.5 kB26 - Python Conditional Statements/157 - Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.5 kB24 - Python Basic Python Syntax/147 - The Double Equality Sign English.srt 2.4 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/217 - real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods Linear Regression with sklearn/221 - real-estate-price-size-year.csv 2.4 kB51 - Deep Learning Business Case Example/352 - Business Case Outlining the Solution English.srt 2.4 kB58 - Case Study Preprocessing the Absenteeismdata/423 - Dropping a Dummy Variable from the Data Set.html 2.4 kB5 - The Field of Data Science Popular Data Science Techniques/14 - Real Life Examples of Big Data English.srt 2.4 kB58 - Case Study Preprocessing the Absenteeismdata/429 - Reordering Columns in a Pandas DataFrame in Python English.srt 2.4 kB49 - Deep Learning Preprocessing/335 - Types of Basic Preprocessing English.srt 2.4 kB20 - Statistics Hypothesis Testing/121 - Further Reading on Null and Alternative Hypothesis.html 2.3 kB23 - Python Variables and Data Types/144 - Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/371 - A Note on Installing Packages in Anaconda.html 2.3 kB64 - Appendix Working with Text Files in Python/502 - Importing-Text-Data-with-NumPy-Template.ipynb 2.3 kB36 - Advanced Statistical Methods Logistic Regression/233 - Introduction to Logistic Regression English.srt 2.3 kB29 - Python Iterations/172 - Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 kB23 - Python Variables and Data Types/143 - Variables-Exercise-Py3.ipynb 2.3 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/389 - MNIST Solutions.html 2.3 kB26 - Python Conditional Statements/155 - Introduction-to-the-If-Statement-Solution-Py3.ipynb 2.2 kB29 - Python Iterations/175 - Iterating-over-Dictionaries-Exercise-Py3.ipynb 2.2 kB24 - Python Basic Python Syntax/151 - Indexing-Elements-Solution-Py3.ipynb 2.2 kB54 - Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset/388 - MNIST Exercises.html 2.2 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - Multiple-linear-regression-and-Adjusted-R-squared.ipynb 2.2 kB64 - Appendix Working with Text Files in Python/496 - Importing-Text-Files-in-Python-open.ipynb 2.2 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/183 - Geometrical Representation of the Linear Regression Model English.srt 2.2 kB59 - Case Study Applying Machine Learning to Create the absenteeismmodule/456 - ARTICLE A Note on pickling.html 2.2 kB28 - Python Sequences/166 - Lists-Exercise-Py3.ipynb 2.2 kB40 - Part 6 Mathematics/279 - Dot-product.ipynb 2.2 kB24 - Python Basic Python Syntax/148 - Reassign-Values-Solution-Py3.ipynb 2.2 kB61 - Case Study Analyzing the Predicted Outputs in Tableau/463 - Absenteeism-predictions.csv 2.2 kB61 - Case Study Analyzing the Predicted Outputs in Tableau/464 - Absenteeism-predictions.csv 2.2 kB58 - Case Study Preprocessing the Absenteeismdata/424 - More on Dummy Variables A Statistical Perspective English.srt 2.1 kB29 - Python Iterations/173 - Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb 2.1 kB36 - Advanced Statistical Methods Logistic Regression/236 - Admittance-regression.ipynb 2.1 kB40 - Part 6 Mathematics/275 - Tensors.ipynb 2.1 kB28 - Python Sequences/168 - Tuples-Exercise-Py3.ipynb 2.1 kB24 - Python Basic Python Syntax/151 - Indexing Elements English.srt 2.1 kB17 - Statistics Inferential Statistics Fundamentals/95 - Introduction English.srt 2.1 kB27 - Python Python Functions/161 - Another-Way-to-Define-a-Function-Solution-Py3.ipynb 2.0 kB50 - Deep Learning Classifying on the MNIST Dataset/349 - MNIST Exercises.html 2.0 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/187 - Using Seaborn for Graphs English.srt 2.0 kB29 - Python Iterations/173 - Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb 2.0 kB29 - Python Iterations/174 - All-In-Solution-Py3.ipynb 1.9 kB60 - Case Study Loading the absenteeismmodule/459 - Absenteeism-new-data.csv 1.9 kB60 - Case Study Loading the absenteeismmodule/459 - scaler 1.9 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/186 - real-estate-price-size.csv 1.9 kB34 - Advanced Statistical Methods Linear Regression with sklearn/210 - real-estate-price-size.csv 1.9 kB27 - Python Python Functions/164 - Functions Containing a Few Arguments English.srt 1.9 kB39 - Advanced Statistical Methods Other Types of Clustering/270 - Heatmaps.ipynb 1.9 kB10 - Probability Combinatorics/29 - Fundamentals of Combinatorics English.srt 1.8 kB29 - Python Iterations/170 - For-Loops-Solution-Py3.ipynb 1.8 kB30 - Python Advanced Python Tools/177 - Modules and Packages English.srt 1.8 kB27 - Python Python Functions/160 - Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb 1.8 kB26 - Python Conditional Statements/156 - Add-an-Else-Statement-Lecture-Py3.ipynb 1.8 kB26 - Python Conditional Statements/157 - Else-If-for-Brief-Elif-Exercise-Py3.ipynb 1.8 kB29 - Python Iterations/171 - While-Loops-and-Incrementing-Solution-Py3.ipynb 1.8 kB44 - Deep Learning TensorFlow 20 Introduction/303 - A Note on TensorFlow 2 Syntax English.srt 1.8 kB27 - Python Python Functions/164 - Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb 1.8 kB24 - Python Basic Python Syntax/148 - Reassign-Values-Exercise-Py3.ipynb 1.7 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/299 - Basic NN Example Exercises.html 1.7 kB44 - Deep Learning TensorFlow 20 Introduction/304 - TensorFlow-Minimal-example-Part1.ipynb 1.7 kB27 - Python Python Functions/163 - Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb 1.7 kB24 - Python Basic Python Syntax/148 - How to Reassign Values English.srt 1.7 kB29 - Python Iterations/174 - All-In-Lecture-Py3.ipynb 1.7 kB25 - Python Other Python Operators/153 - Comparison-Operators-Exercise-Py3.ipynb 1.6 kB53 - Appendix Deep Learning TensorFlow 1 Introduction/378 - Basic NN Example with TF Exercises.html 1.6 kB27 - Python Python Functions/162 - 0.6.4-Using-a-Function-in-another-Function-Solution-Py3.ipynb 1.6 kB27 - Python Python Functions/160 - Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb 1.6 kB36 - Advanced Statistical Methods Logistic Regression/234 - 2.01.Admittance.csv 1.6 kB64 - Appendix Working with Text Files in Python/498 - Importing.csv-Files-with-pandas-Part-I.ipynb 1.6 kB26 - Python Conditional Statements/155 - Introduction-to-the-If-Statement-Exercise-Py3.ipynb 1.6 kB24 - Python Basic Python Syntax/150 - Line-Continuation-Solution-Py3.ipynb 1.5 kB24 - Python Basic Python Syntax/152 - Structure-Your-Code-with-Indentation-Solution-Py3.ipynb 1.5 kB29 - Python Iterations/172 - Create-Lists-with-the-range-Function-Exercise-Py3.ipynb 1.5 kB24 - Python Basic Python Syntax/147 - The-Double-Equality-Sign-Lecture-Py3.ipynb 1.5 kB24 - Python Basic Python Syntax/150 - Understanding Line Continuation English.srt 1.5 kB26 - Python Conditional Statements/156 - Add-an-Else-Statement-Solution-Py3.ipynb 1.4 kB64 - Appendix Working with Text Files in Python/516 - Working with Text Files in Python Conclusion English.srt 1.4 kB24 - Python Basic Python Syntax/151 - Indexing-Elements-Exercise-Py3.ipynb 1.4 kB29 - Python Iterations/172 - Create-Lists-with-the-range-Function-Lecture-Py3.ipynb 1.4 kB32 - Advanced Statistical Methods Linear Regression with StatsModels/186 - First Regression in Python Exercise.html 1.4 kB24 - Python Basic Python Syntax/151 - Indexing-Elements-Lecture-Py3.ipynb 1.3 kB29 - Python Iterations/174 - All-In-Exercise-Py3.ipynb 1.3 kB44 - Deep Learning TensorFlow 20 Introduction/308 - Basic NN with TensorFlow Exercises.html 1.3 kB27 - Python Python Functions/163 - Combining-Conditional-Statements-and-Functions-Lecture-Py3.ipynb 1.3 kB29 - Python Iterations/170 - For-Loops-Exercise-Py3.ipynb 1.3 kB29 - Python Iterations/170 - For-Loops-Lecture-Py3.ipynb 1.3 kB27 - Python Python Functions/161 - Another-Way-to-Define-a-Function-Exercise-Py3.ipynb 1.3 kB58 - Case Study Preprocessing the Absenteeismdata/438 - EXERCISE Removing the Date Column.html 1.2 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/202 - 1.03.Dummies.csv 1.2 kB43 - Deep Learning How to Build a Neural Network from Scratch with NumPy/295 - Minimal-example-Part-1.ipynb 1.2 kB27 - Python Python Functions/160 - Creating-a-Function-with-a-Parameter-Exercise-Py3.ipynb 1.2 kB26 - Python Conditional Statements/155 - Introduction-to-the-If-Statement-Lecture-Py3.ipynb 1.2 kB24 - Python Basic Python Syntax/147 - The-Double-Equality-Sign-Solution-Py3.ipynb 1.2 kB24 - Python Basic Python Syntax/150 - Line-Continuation-Exercise-Py3.ipynb 1.2 kB29 - Python Iterations/171 - While-Loops-and-Incrementing-Exercise-Py3.ipynb 1.1 kB33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/193 - 1.02.Multiple-linear-regression.csv 1.1 kB29 - Python Iterations/171 - While-Loops-and-Incrementing-Lecture-Py3.ipynb 1.1 kB29 - Python Iterations/175 - Iterating-over-Dictionaries-Lecture-Py3.ipynb 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/211 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/212 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/213 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/214 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/215 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/216 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/218 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/219 - 1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods Linear Regression with sklearn/220 - 1.02.Multiple-linear-regression.csv 1.1 kB27 - Python Python Functions/163 - Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb 1.1 kB52 - Deep Learning Conclusion/365 - DeepMind and Deep Learning.html 1.1 kB27 - Python Python Functions/162 - 0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb 1.1 kB24 - Python Basic Python Syntax/149 - Add-Comments-Lecture-Py3.ipynb 1.1 kB26 - Python Conditional Statements/156 - Add-an-Else-Statement-Exercise-Py3.ipynb 1.0 kB60 - Case Study Loading the absenteeismmodule/459 - model 1.0 kB27 - Python Python Functions/162 - 0.6.4-Using-a-Function-in-another-Function-Lecture-Py3.ipynb 1.0 kB60 - Case Study Loading the absenteeismmodule/462 - Exporting the Obtained Data Set as a csv.html 998 Bytes60 - Case Study Loading the absenteeismmodule/462 - Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb 973 Bytes24 - Python Basic Python Syntax/152 - Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb 958 Bytes24 - Python Basic Python Syntax/152 - Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb 956 Bytes32 - Advanced Statistical Methods Linear Regression with StatsModels/185 - 1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/207 - 1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/208 - 1.01.Simple-linear-regression.csv 922 Bytes58 - Case Study Preprocessing the Absenteeismdata/442 - A Note on Exporting Your Data as a csv File.html 883 Bytes58 - Case Study Preprocessing the Absenteeismdata/417 - EXERCISE Dropping a Column from a DataFrame in Python.html 870 Bytes27 - Python Python Functions/159 - Defining-a-Function-in-Python-Lecture-Py3.ipynb 868 Bytes35 - Advanced Statistical Methods Practical Example Linear Regression/226 - A Note on Multicollinearity.html 849 Bytes24 - Python Basic Python Syntax/147 - The-Double-Equality-Sign-Exercise-Py3.ipynb 838 Bytes26 - Python Conditional Statements/158 - A-Note-on-Boolean-Values-Lecture-Py3.ipynb 791 Bytes24 - Python Basic Python Syntax/150 - Line-Continuation-Lecture-Py3.ipynb 779 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/209 - A Note on Normalization.html 733 Bytes35 - Advanced Statistical Methods Practical Example Linear Regression/230 - Dummy Variables Exercise.html 713 Bytes53 - Appendix Deep Learning TensorFlow 1 Introduction/369 - READ ME.html 564 Bytes61 - Case Study Analyzing the Predicted Outputs in Tableau/467 - EXERCISE Transportation Expense vs Probability.html 553 Bytes45 - Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks/317 - Backpropagation A Peek into the Mathematics of Optimization.html 543 Bytes15 - Statistics Descriptive Statistics/86 - Variance Exercise.html 522 Bytes60 - Case Study Loading the absenteeismmodule/459 - Are You Sure Youre All Set.html 519 Bytes35 - Advanced Statistical Methods Practical Example Linear Regression/232 - Linear Regression Exercise.html 503 Bytes58 - Case Study Preprocessing the Absenteeismdata/431 - SOLUTION Reordering Columns in a Pandas DataFrame in Python.html 478 Bytes55 - Appendix Deep Learning TensorFlow 1 Business Case/401 - Business Case Final Exercise.html 443 Bytes51 - Deep Learning Business Case Example/362 - Business Case Final Exercise.html 433 Bytes61 - Case Study Analyzing the Predicted Outputs in Tableau/465 - EXERCISE Reasons vs Probability.html 397 Bytes55 - Appendix Deep Learning TensorFlow 1 Business Case/394 - Business Case Preprocessing Exercise.html 389 Bytes61 - Case Study Analyzing the Predicted Outputs in Tableau/463 - EXERCISE Age vs Probability.html 385 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/215 - A Note on Calculation of Pvalues with sklearn.html 372 Bytes51 - Deep Learning Business Case Example/355 - Business Case Preprocessing the Data Exercise.html 370 Bytes36 - Advanced Statistical Methods Logistic Regression/247 - 2.03.Test-dataset.csv 322 Bytes64 - Appendix Working with Text Files in Python/504 - Importing Data with NumPy Exercise.html 308 Bytes59 - Case Study Applying Machine Learning to Create the absenteeismmodule/457 - EXERCISE Saving the Model and Scaler.html 284 Bytes38 - Advanced Statistical Methods KMeans Clustering/263 - 3.12.Example.csv 283 Bytes64 - Appendix Working with Text Files in Python/515 - Saving Data with Numpy Exercise.html 260 Bytes39 - Advanced Statistical Methods Other Types of Clustering/270 - Country-clusters-standardized.csv 244 Bytes38 - Advanced Statistical Methods KMeans Clustering/254 - 3.01.Country-clusters.csv 200 Bytes51 - Deep Learning Business Case Example/360 - Setting an Early Stopping Mechanism Exercise.html 192 Bytes58 - Case Study Preprocessing the Absenteeismdata/427 - EXERCISE Using concat in Python.html 189 Bytes58 - Case Study Preprocessing the Absenteeismdata/430 - EXERCISE Reordering Columns in a Pandas DataFrame in Python.html 167 Bytes
59 - Case Study Applying Machine Learning to Create the absenteeismmodule/453 - Logistic Regression prior to Backward Elimination.txt 165 Bytes59 - Case Study Applying Machine Learning to Create the absenteeismmodule/451 - Logistic Regression prior to Custom Scaler.txt 158 Bytes59 - Case Study Applying Machine Learning to Create the absenteeismmodule/457 - Logistic Regression with Comments.txt 149 Bytes58 - Case Study Preprocessing the Absenteeismdata/428 - SOLUTION Using concat in Python.html 143 Bytes58 - Case Study Preprocessing the Absenteeismdata/433 - EXERCISE Creating Checkpoints while Coding in Jupyter.html 137 Bytes59 - Case Study Applying Machine Learning to Create the absenteeismmodule/457 - Logistic Regression.txt 135 Bytes58 - Case Study Preprocessing the Absenteeismdata/421 - EXERCISE Obtaining Dummies from a Single Feature.html 129 Bytes
0. Websites you may like/[CourseClub.Me].url 122 Bytes13 - Probability Probability in Other Fields/[CourseClub.Me].url 122 Bytes29 - Python Iterations/[CourseClub.Me].url 122 Bytes39 - Advanced Statistical Methods Other Types of Clustering/[CourseClub.Me].url 122 Bytes52 - Deep Learning Conclusion/[CourseClub.Me].url 122 Bytes[CourseClub.Me].url 122 Bytes58 - Case Study Preprocessing the Absenteeismdata/434 - SOLUTION Creating Checkpoints while Coding in Jupyter.html 118 Bytes58 - Case Study Preprocessing the Absenteeismdata/422 - SOLUTION Obtaining Dummies from a Single Feature.html 117 Bytes58 - Case Study Preprocessing the Absenteeismdata/418 - SOLUTION Dropping a Column from a DataFrame in Python.html 114 Bytes36 - Advanced Statistical Methods Logistic Regression/237 - Building a Logistic Regression Exercise.html 87 Bytes36 - Advanced Statistical Methods Logistic Regression/240 - Understanding Logistic Regression Tables Exercise.html 87 Bytes36 - Advanced Statistical Methods Logistic Regression/243 - Binary Predictors in a Logistic Regression Exercise.html 87 Bytes36 - Advanced Statistical Methods Logistic Regression/245 - Calculating the Accuracy of the Model.html 87 Bytes36 - Advanced Statistical Methods Logistic Regression/248 - Testing the Model Exercise.html 87 Bytes38 - Advanced Statistical Methods KMeans Clustering/255 - A Simple Example of Clustering Exercise.html 87 Bytes38 - Advanced Statistical Methods KMeans Clustering/257 - Clustering Categorical Data Exercise.html 87 Bytes38 - Advanced Statistical Methods KMeans Clustering/259 - How to Choose the Number of Clusters Exercise.html 87 Bytes38 - Advanced Statistical Methods KMeans Clustering/266 - EXERCISE Species Segmentation with Cluster Analysis Part 1.html 87 Bytes38 - Advanced Statistical Methods KMeans Clustering/267 - EXERCISE Species Segmentation with Cluster Analysis Part 2.html 87 Bytes15 - Statistics Descriptive Statistics/74 - Categorical Variables Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/76 - Numerical Variables Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/78 - Histogram Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/80 - Cross Tables and Scatter Plots Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/82 - Mean Median and Mode Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/84 - Skewness Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/88 - Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/90 - Covariance Exercise.html 81 Bytes15 - Statistics Descriptive Statistics/92 - Correlation Coefficient Exercise.html 81 Bytes16 - Statistics Practical Example Descriptive Statistics/94 - Practical Example Descriptive Statistics Exercise.html 81 Bytes17 - Statistics Inferential Statistics Fundamentals/99 - The Standard Normal Distribution Exercise.html 81 Bytes18 - Statistics Inferential Statistics Confidence Intervals/105 - Confidence Intervals Population Variance Known Zscore Exercise.html 81 Bytes18 - Statistics Inferential Statistics Confidence Intervals/109 - Confidence Intervals Population Variance Unknown Tscore Exercise.html 81 Bytes18 - Statistics Inferential Statistics Confidence Intervals/112 - Confidence intervals Two means Dependent samples Exercise.html 81 Bytes18 - Statistics Inferential Statistics Confidence Intervals/114 - Confidence intervals Two means Independent Samples Part 1 Exercise.html 81 Bytes18 - Statistics Inferential Statistics Confidence Intervals/116 - Confidence intervals Two means Independent Samples Part 2 Exercise.html 81 Bytes19 - Statistics Practical Example Inferential Statistics/119 - Practical Example Inferential Statistics Exercise.html 81 Bytes20 - Statistics Hypothesis Testing/125 - Test for the Mean Population Variance Known Exercise.html 81 Bytes20 - Statistics Hypothesis Testing/128 - Test for the Mean Population Variance Unknown Exercise.html 81 Bytes20 - Statistics Hypothesis Testing/130 - Test for the Mean Dependent Samples Exercise.html 81 Bytes20 - Statistics Hypothesis Testing/132 - Test for the mean Independent Samples Part 1 Exercise.html 81 Bytes20 - Statistics Hypothesis Testing/134 - Test for the mean Independent Samples Part 2 Exercise.html 81 Bytes21 - Statistics Practical Example Hypothesis Testing/136 - Practical Example Hypothesis Testing Exercise.html 81 Bytes50 - Deep Learning Classifying on the MNIST Dataset/343 - MNIST Preprocess the Data Scale the Test Data Exercise.html 79 Bytes50 - Deep Learning Classifying on the MNIST Dataset/345 - MNIST Preprocess the Data Shuffle and Batch Exercise.html 79 Bytes51 - Deep Learning Business Case Example/357 - Business Case Load the Preprocessed Data Exercise.html 79 Bytes33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/194 - Multiple Linear Regression Exercise.html 76 Bytes33 - Advanced Statistical Methods Multiple Linear Regression with StatsModels/203 - Dealing with Categorical Data Dummy Variables.html 76 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/210 - Simple Linear Regression with sklearn Exercise.html 76 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/213 - Calculating the Adjusted RSquared in sklearn Exercise.html 76 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/217 - Multiple Linear Regression Exercise.html 76 Bytes34 - Advanced Statistical Methods Linear Regression with sklearn/221 - Feature Scaling Standardization Exercise.html 76 Bytes35 - Advanced Statistical Methods Practical Example Linear Regression/228 - Dummies and Variance Inflation Factor Exercise.html 76 Bytes1 - Part 1 Introduction/3 - Download all resources.txt 73 Bytes35 - Advanced Statistical Methods Practical Example Linear Regression/227 - sklearn Linear Regression Practical Example Part 3.txt 73 Bytes64 - Appendix Working with Text Files in Python/488 - Section Resources Working with Text Files.txt 73 Bytes0. Websites you may like/[GigaCourse.Com].url 49 Bytes13 - Probability Probability in Other Fields/[GigaCourse.Com].url 49 Bytes29 - Python Iterations/[GigaCourse.Com].url 49 Bytes39 - Advanced Statistical Methods Other Types of Clustering/[GigaCourse.Com].url 49 Bytes52 - Deep Learning Conclusion/[GigaCourse.Com].url 49 Bytes[GigaCourse.Com].url 49 Bytes64 - Appendix Working with Text Files in Python/496 - source.txt 39 Bytes64 - Appendix Working with Text Files in Python/497 - source.txt 39 Bytes
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