magnet:?xt=urn:btih:C48C0FC3AC45DE1341E3E6786F3A5E00B0561062
12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4 145.0 MB11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4 131.6 MB05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4 110.6 MB40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful_.mp4 90.4 MB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1).mp4 89.0 MB20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp4 84.8 MB05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp4 78.4 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing.mp4 78.0 MB51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data.mp4 77.4 MB56 - Software Integration/003 Taking a Closer Look at APIs.mp4 68.5 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp4 66.9 MB05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp4 64.8 MB05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp4 63.4 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset.mp4 63.2 MB56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_.mp4 61.7 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider.mp4 59.0 MB02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many_.mp4 57.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp4 56.9 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp4 54.7 MB51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 53.9 MB05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp4 53.8 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp4 53.8 MB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5).mp4 52.9 MB02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 52.4 MB01 - Part 1_ Introduction/002 What Does the Course Cover.mp4 52.1 MB05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp4 50.1 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp4 47.2 MB36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp4 46.1 MB51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism.mp4 45.9 MB62 - Appendix - Additional Python Tools/005 List Comprehensions.mp4 45.3 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline.mp4 44.5 MB15 - Statistics - Descriptive Statistics/001 Types of Data.mp4 44.5 MB10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp4 44.3 MB56 - Software Integration/005 Software Integration - Explained.mp4 44.0 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp4 43.3 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp4 42.2 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp4 42.1 MB13 - Probability - Probability in Other Fields/001 Probability in Finance.mp4 41.6 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column.mp4 40.8 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp4 40.6 MB20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp4 40.1 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing.mp4 40.0 MB63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp4 39.1 MB16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics.mp4 39.0 MB20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp4 38.8 MB15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp4 38.4 MB38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_.mp4 38.3 MB09 - Part 2_ Probability/003 Frequency.mp4 38.2 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp4 37.9 MB02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp4 37.7 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp4 37.3 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp4 37.0 MB37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp4 36.8 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables.mp4 36.8 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline.mp4 36.4 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp4 36.1 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp4 35.9 MB38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp4 35.7 MB02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp4 35.6 MB62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp4 35.4 MB20 - Statistics - Hypothesis Testing/007 p-value.mp4 34.7 MB22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter.mp4 34.5 MB20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp4 34.4 MB50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch.mp4 34.3 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression.mp4 34.1 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model.mp4 33.5 MB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2).mp4 33.5 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning.mp4 33.4 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp4 33.2 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp4 33.2 MB15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp4 33.0 MB50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning.mp4 32.5 MB52 - Deep Learning - Conclusion/004 An overview of CNNs.mp4 31.9 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp4 31.5 MB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4).mp4 31.3 MB63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp4 31.2 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp4 31.1 MB09 - Part 2_ Probability/002 Computing Expected Values.mp4 30.7 MB09 - Part 2_ Probability/001 The Basic Probability Formula.mp4 30.5 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 30.3 MB12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp4 30.3 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp4 30.1 MB12 - Probability - Distributions/002 Types of Probability Distributions.mp4 30.1 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module.mp4 30.0 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals_.mp4 29.8 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 29.4 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp4 29.4 MB51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result.mp4 29.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp4 29.0 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity.mp4 28.7 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0.mp4 28.7 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp4 28.5 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2.mp4 28.3 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept.mp4 28.3 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization.mp4 28.3 MB40 - Part 6_ Mathematics/010 Dot Product of Matrices.mp4 27.7 MB63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp4 27.6 MB51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset.mp4 27.5 MB38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp4 27.3 MB60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp4 27.3 MB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp4 27.0 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment.mp4 26.8 MB12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution.mp4 26.2 MB28 - Python - Sequences/005 Dictionaries.mp4 26.1 MB20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp4 25.7 MB13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp4 25.1 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp4 24.8 MB29 - Python - Iterations/001 For Loops.mp4 24.7 MB63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp4 24.7 MB28 - Python - Sequences/002 Using Methods.mp4 24.6 MB50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp4 24.0 MB17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp4 24.0 MB42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent.mp4 23.8 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error.mp4 23.8 MB50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model.mp4 23.7 MB05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp4 23.5 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_.mp4 23.5 MB63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp4 23.3 MB19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics.mp4 23.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model.mp4 23.2 MB40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices.mp4 23.1 MB29 - Python - Iterations/004 Conditional Statements and Loops.mp4 23.0 MB36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp4 23.0 MB03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 22.8 MB62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp4 22.7 MB36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp4 22.6 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset.mp4 22.6 MB05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp4 22.2 MB38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp4 22.2 MB11 - Probability - Bayesian Inference/011 Bayes' Law.mp4 22.0 MB63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp4 21.7 MB12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution.mp4 21.7 MB28 - Python - Sequences/001 Lists.mp4 21.5 MB40 - Part 6_ Mathematics/008 Transpose of a Matrix.mp4 21.5 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp4 21.4 MB36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp4 21.3 MB15 - Statistics - Descriptive Statistics/015 Variance.mp4 21.2 MB29 - Python - Iterations/002 While Loops and Incrementing.mp4 21.2 MB15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp4 21.1 MB11 - Probability - Bayesian Inference/010 The Multiplication Law.mp4 20.8 MB38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp4 20.8 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp4 20.7 MB23 - Python - Variables and Data Types/003 Python Strings.mp4 20.7 MB20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp4 20.7 MB15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp4 20.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets_.mp4 20.6 MB12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution.mp4 20.6 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework.mp4 20.6 MB06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp4 20.5 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation.mp4 20.4 MB11 - Probability - Bayesian Inference/004 Union of Sets.mp4 20.4 MB62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp4 20.3 MB15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp4 20.3 MB05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp4 20.3 MB12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp4 20.2 MB28 - Python - Sequences/003 List Slicing.mp4 20.1 MB56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp4 20.1 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net.mp4 20.1 MB11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp4 19.9 MB40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.9 MB25 - Python - Other Python Operators/002 Logical and Identity Operators.mp4 19.9 MB10 - Probability - Combinatorics/006 Solving Combinations.mp4 19.9 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation.mp4 19.5 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications.mp4 19.5 MB36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp4 19.4 MB15 - Statistics - Descriptive Statistics/019 Covariance.mp4 19.3 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp4 19.2 MB20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp4 19.1 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp4 18.9 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp4 18.9 MB63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp4 18.7 MB15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp4 18.4 MB11 - Probability - Bayesian Inference/001 Sets and Events.mp4 18.3 MB47 - Deep Learning - Initialization/001 What is Initialization_.mp4 18.3 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp4 18.2 MB39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp4 18.2 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output.mp4 17.9 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp4 17.9 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp4 17.7 MB17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp4 17.7 MB63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp4 17.6 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model.mp4 17.6 MB36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp4 17.6 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp4 17.5 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro.mp4 17.4 MB29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 17.3 MB08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 17.2 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.mp4 17.2 MB42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent.mp4 17.1 MB11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp4 17.1 MB21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing.mp4 17.1 MB42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp4 17.0 MB10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery.mp4 16.9 MB17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp4 16.9 MB17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp4 16.9 MB12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution.mp4 16.7 MB57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 16.6 MB12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution.mp4 16.5 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent.mp4 16.5 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp4 16.4 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp4 16.4 MB52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp4 16.4 MB
64 - Bonus Lecture/35215106-365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 16.3 MB63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp4 16.2 MB22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp4 16.1 MB57 - Case Study - What's Next in the Course_/003 Introducing the Data Set.mp4 16.0 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp4 16.0 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression.mp4 16.0 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp4 15.9 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2.mp4 15.7 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp4 15.7 MB12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution.mp4 15.5 MB10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp4 15.5 MB12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution.mp4 15.3 MB29 - Python - Iterations/003 Lists with the range() Function.mp4 15.2 MB22 - Part 4_ Introduction to Python/001 Introduction to Programming.mp4 15.0 MB13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp4 15.0 MB26 - Python - Conditional Statements/003 The ELIF Statement.mp4 14.9 MB10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp4 14.7 MB10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp4 14.6 MB05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp4 14.6 MB51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data.mp4 14.5 MB10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp4 14.4 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp4 14.3 MB40 - Part 6_ Mathematics/003 Linear Algebra and Geometry.mp4 14.2 MB46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp4 14.2 MB10 - Probability - Combinatorics/007 Symmetry of Combinations.mp4 14.2 MB17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp4 14.0 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution.mp4 14.0 MB63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp4 13.8 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp4 13.8 MB01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp4 13.7 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp4 13.7 MB36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp4 13.5 MB10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp4 13.5 MB15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp4 13.4 MB04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 13.0 MB62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.9 MB50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 12.8 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise.mp4 12.8 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 12.6 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.6 MB10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp4 12.6 MB11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp4 12.6 MB49 - Deep Learning - Preprocessing/003 Standardization.mp4 12.5 MB22 - Part 4_ Introduction to Python/002 Why Python_.mp4 12.3 MB40 - Part 6_ Mathematics/001 What is a Matrix_.mp4 12.3 MB40 - Part 6_ Mathematics/005 What is a Tensor_.mp4 12.2 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp4 12.1 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm.mp4 12.1 MB09 - Part 2_ Probability/004 Events and Their Complements.mp4 12.0 MB11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp4 11.9 MB36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp4 11.9 MB40 - Part 6_ Mathematics/009 Dot Product.mp4 11.9 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp4 11.6 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net_.mp4 11.6 MB12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution.mp4 11.5 MB38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp4 11.5 MB11 - Probability - Bayesian Inference/009 The Additive Rule.mp4 11.4 MB14 - Part 3_ Statistics/001 Population and Sample.mp4 11.4 MB61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp4 11.4 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp4 11.3 MB37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp4 11.2 MB50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer.mp4 11.2 MB63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp4 11.1 MB38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp4 11.0 MB38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp4 11.0 MB46 - Deep Learning - Overfitting/001 What is Overfitting_.mp4 11.0 MB42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp4 10.9 MB38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp4 10.8 MB12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution.mp4 10.6 MB15 - Statistics - Descriptive Statistics/013 Skewness.mp4 10.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp4 10.4 MB42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp4 10.3 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp4 10.3 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp4 10.2 MB42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss.mp4 10.2 MB52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp4 10.1 MB37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp4 10.0 MB28 - Python - Sequences/004 Tuples.mp4 10.0 MB07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp4 9.9 MB56 - Software Integration/004 Communication between Software Products through Text Files.mp4 9.7 MB12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp4 9.7 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column.mp4 9.6 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity.mp4 9.4 MB49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp4 9.4 MB23 - Python - Variables and Data Types/001 Variables.mp4 9.4 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors.mp4 9.3 MB11 - Probability - Bayesian Inference/003 Intersection of Sets.mp4 9.2 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping.mp4 9.1 MB
12 - Probability - Distributions/17971238-FIFA19.csv 9.1 MB12 - Probability - Distributions/17971248-FIFA19-post.csv 9.1 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp4 9.0 MB17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp4 9.0 MB36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp4 9.0 MB30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp4 8.9 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions.mp4 8.9 MB27 - Python - Python Functions/007 Built-in Functions in Python.mp4 8.9 MB46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp4 8.9 MB30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp4 8.8 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation.mp4 8.8 MB40 - Part 6_ Mathematics/002 Scalars and Vectors.mp4 8.8 MB60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp4 8.8 MB49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp4 8.8 MB27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp4 8.7 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.6 MB51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model.mp4 8.6 MB46 - Deep Learning - Overfitting/003 What is Validation_.mp4 8.5 MB02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp4 8.4 MB22 - Part 4_ Introduction to Python/003 Why Jupyter_.mp4 8.4 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages.mp4 8.3 MB42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp4 8.3 MB62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp4 8.2 MB42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp4 8.1 MB46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp4 8.1 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp4 8.1 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture.mp4 8.1 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation.mp4 8.0 MB50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp4 8.0 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp4 8.0 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545318-Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 8.0 MB39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp4 7.9 MB20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp4 7.9 MB42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp4 7.9 MB41 - Part 7_ Deep Learning/001 What to Expect from this Part_.mp4 7.9 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp4 7.7 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity.mp4 7.7 MB24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp4 7.6 MB36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp4 7.6 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow.mp4 7.6 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp4 7.5 MB
02 - The Field of Data Science - The Various Data Science Disciplines/13075166-365-DataScience.png 7.3 MB02 - The Field of Data Science - The Various Data Science Disciplines/13075168-365-DataScience.png 7.3 MB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3).mp4 7.2 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp4 7.2 MB57 - Case Study - What's Next in the Course_/002 The Business Task.mp4 7.1 MB52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp4 7.1 MB27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp4 6.8 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp4 6.8 MB42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp4 6.7 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.mp4 6.5 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow.mp4 6.5 MB27 - Python - Python Functions/005 Conditional Statements and Functions.mp4 6.3 MB42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_.mp4 6.3 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp4 6.2 MB63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp4 6.1 MB47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp4 6.0 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp4 6.0 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp4 5.8 MB12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution.mp4 5.7 MB49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp4 5.6 MB26 - Python - Conditional Statements/001 The IF Statement.mp4 5.6 MB11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp4 5.5 MB26 - Python - Conditional Statements/002 The ELSE Statement.mp4 5.5 MB46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp4 5.4 MB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp4 5.4 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp4 5.4 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp4 5.2 MB30 - Python - Advanced Python Tools/003 What is the Standard Library_.mp4 5.1 MB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression.mp4 4.9 MB23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp4 4.8 MB37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp4 4.7 MB42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss.mp4 4.7 MB36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp4 4.6 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model.mp4 4.6 MB22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 4.6 MB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset_.mp4 4.4 MB05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp4 4.4 MB47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 4.4 MB18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp4 4.4 MB50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset.mp4 4.3 MB34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_.mp4 4.2 MB15 - Statistics - Descriptive Statistics/007 The Histogram.mp4 4.0 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp4 3.9 MB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1.mp4 3.9 MB52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp4 3.9 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp4 3.7 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer_.mp4 3.6 MB40 - Part 6_ Mathematics/007 Errors when Adding Matrices.mp4 3.5 MB26 - Python - Conditional Statements/004 A Note on Boolean Values.mp4 3.4 MB27 - Python - Python Functions/004 How to Use a Function within a Function.mp4 3.4 MB27 - Python - Python Functions/001 Defining a Function in Python.mp4 3.4 MB10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp4 3.4 MB58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables_ A Statistical Perspective.mp4 3.3 MB25 - Python - Other Python Operators/001 Comparison Operators.mp4 3.3 MB17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp4 3.1 MB31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp4 3.1 MB29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp4 3.1 MB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution.mp4 3.0 MB24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp4 2.9 MB24 - Python - Basic Python Syntax/002 The Double Equality Sign.mp4 2.8 MB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1_ Linearity.mp4 2.8 MB38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp4 2.5 MB24 - Python - Basic Python Syntax/004 Add Comments.mp4 2.5 MB49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp4 2.5 MB24 - Python - Basic Python Syntax/006 Indexing Elements.mp4 2.5 MB44 - Deep Learning - TensorFlow 2.0_ Introduction/004 A Note on TensorFlow 2 Syntax.mp4 2.5 MB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp4 2.5 MB27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp4 2.4 MB51 - Deep Learning - Business Case Example/002 Business Case_ Outlining the Solution.mp4 2.3 MB23 - Python - Variables and Data Types/15870664-Python-Introduction-Course-Notes.pdf 2.1 MB24 - Python - Basic Python Syntax/003 How to Reassign Values.mp4 2.0 MB19 - Statistics - Practical Example_ Inferential Statistics/17959058-3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model.mp4 1.8 MB19 - Statistics - Practical Example_ Inferential Statistics/13056326-3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.8 MB19 - Statistics - Practical Example_ Inferential Statistics/17959056-3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.8 MB30 - Python - Advanced Python Tools/002 Modules and Packages.mp4 1.8 MB20 - Statistics - Hypothesis Testing/16753580-Online-p-value-calculator.pdf 1.2 MB24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp4 1.0 MB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/13070016-Course-Notes-Section-6.pdf 958.9 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/13070018-Course-Notes-Section-6.pdf 958.9 kB11 - Probability - Bayesian Inference/17970686-CDS-2017-2018-Hamilton.pdf 865.6 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588630-sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB51 - Deep Learning - Business Case Example/19664156-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/13070978-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591716-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591732-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591808-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591842-Audiobooks-data.csv 727.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591940-Audiobooks-data.csv 727.8 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588626-sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 715.1 kB20 - Statistics - Hypothesis Testing/22431075-Course-notes-hypothesis-testing.pdf 672.2 kB20 - Statistics - Hypothesis Testing/22431079-Course-notes-hypothesis-testing.pdf 672.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/13070602-Shortcuts-for-Jupyter.pdf 634.0 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/13070604-Shortcuts-for-Jupyter.pdf 634.0 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/13070608-Shortcuts-for-Jupyter.pdf 634.0 kB42 - Deep Learning - Introduction to Neural Networks/16752952-Course-Notes-Section-2.pdf 592.0 kB42 - Deep Learning - Introduction to Neural Networks/16752958-Course-Notes-Section-2.pdf 592.0 kB14 - Part 3_ Statistics/14812652-Course-notes-descriptive-statistics.pdf 493.8 kB15 - Statistics - Descriptive Statistics/14812660-Course-notes-descriptive-statistics.pdf 493.8 kB12 - Probability - Distributions/20945990-Course-Notes-Probability-Distributions.pdf 475.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588618-sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 417.4 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588612-sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 406.8 kB11 - Probability - Bayesian Inference/17431622-Course-Notes-Bayesian-Inference.pdf 395.3 kB17 - Statistics - Inferential Statistics Fundamentals/13831264-Course-notes-inferential-statistics.pdf 391.5 kB17 - Statistics - Inferential Statistics Fundamentals/13831266-Course-notes-inferential-statistics.pdf 391.5 kB09 - Part 2_ Probability/17431614-Course-Notes-Basic-Probability.pdf 380.0 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588602-sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 379.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588558-sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 359.9 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588604-sklearn-Dummies-and-VIF-Exercise.ipynb 352.9 kB12 - Probability - Distributions/17431628-Solving-Integrals.pdf 352.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588552-sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 351.8 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588466-sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 343.7 kB36 - Advanced Statistical Methods - Logistic Regression/23412976-Course-Notes-Logistic-Regression.pdf 343.2 kB36 - Advanced Statistical Methods - Logistic Regression/23413016-Course-Notes-Logistic-Regression.pdf 343.2 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588462-sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB02 - The Field of Data Science - The Various Data Science Disciplines/13075156-365-DataScience-Diagram.pdf 330.8 kB02 - The Field of Data Science - The Various Data Science Disciplines/13075162-365-DataScience-Diagram.pdf 330.8 kB13 - Probability - Probability in Other Fields/23224540-Probability-Cheat-Sheet.pdf 328.0 kB31 - Part 5_ Advanced Statistical Methods in Python/22685780-Course-notes-regression-analysis.pdf 319.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/22685784-Course-notes-regression-analysis.pdf 319.7 kB01 - Part 1_ Introduction/16507136-FAQ-The-Data-Science-Course.pdf 313.4 kB15 - Statistics - Descriptive Statistics/16753694-Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB15 - Statistics - Descriptive Statistics/16753696-Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB10 - Probability - Combinatorics/19540858-Additional-Exercises-Combinatorics-Solutions.pdf 251.6 kB10 - Probability - Combinatorics/17431618-Course-Notes-Combinatorics.pdf 231.5 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588446-1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588460-1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588598-1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588606-1.04.Real-life-example.csv 225.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588624-1.04.Real-life-example.csv 225.1 kB37 - Advanced Statistical Methods - Cluster Analysis/23413656-Course-Notes-Cluster-Analysis.pdf 213.7 kB37 - Advanced Statistical Methods - Cluster Analysis/23413662-Course-Notes-Cluster-Analysis.pdf 213.7 kB10 - Probability - Combinatorics/17550452-Combinations-With-Repetition.pdf 212.4 kB13 - Probability - Probability in Other Fields/19327648-Probability-in-Finance-Solutions.pdf 188.9 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/21993772-Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588454-sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 175.5 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/29588452-sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 170.9 kB16 - Statistics - Practical Example_ Descriptive Statistics/13129220-2.13.Practical-example.Descriptive-statistics-lesson.xlsx 150.0 kB16 - Statistics - Practical Example_ Descriptive Statistics/19527576-2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 149.9 kB12 - Probability - Distributions/17862366-Poisson-Expected-Value-and-Variance.pdf 149.5 kB12 - Probability - Distributions/17550252-Normal-Distribution-Exp-and-Var.pdf 147.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/15271322-data-preprocessing-homework.pdf 137.7 kB16 - Statistics - Practical Example_ Descriptive Statistics/19527574-2.13.Practical-example.Descriptive-statistics-exercise.xlsx 123.2 kB36 - Advanced Statistical Methods - Logistic Regression/29588898-Testing-the-Model-Solution.ipynb 113.8 kB13 - Probability - Probability in Other Fields/19327638-Probability-in-Finance-Homework.pdf 113.3 kB10 - Probability - Combinatorics/17756226-Additional-Exercises-Combinatorics.pdf 109.1 kB10 - Probability - Combinatorics/17431624-Symmetry-Explained.pdf 87.1 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589836-TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589288-Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589828-TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 85.7 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589822-TensorFlow-Minimal-example-All-exercises.ipynb 85.6 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589808-TensorFlow-Minimal-example-complete-with-comments.ipynb 84.3 kB36 - Advanced Statistical Methods - Logistic Regression/29588856-Calculating-the-Accuracy-of-the-Model-Solution.ipynb 83.2 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589834-TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 79.4 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589804-TensorFlow-Minimal-example-complete.ipynb 78.7 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589788-TensorFlow-Minimal-example-Part3.ipynb 78.4 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589280-Minimal-example-Exercise-3.c.Solution.ipynb 71.8 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589266-Minimal-example-Exercise-1-Solution.ipynb 70.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589298-Minimal-example-Exercise-5-Solution.ipynb 70.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589274-Minimal-example-Exercise-3.a.Solution.ipynb 69.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589278-Minimal-example-Exercise-3.b.Solution.ipynb 69.3 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589294-Minimal-example-Exercise-4-Solution.ipynb 68.1 kB60 - Case Study - Loading the 'absenteeism_module'/29545372-Absenteeism-Exercise-Integration.ipynb 63.8 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589302-Minimal-example-Exercise-6.ipynb 63.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589304-Minimal-example-Exercise-6-Solution.ipynb 63.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589272-Minimal-example-Exercise-2-Solution.ipynb 62.9 kB21 - Statistics - Practical Example_ Hypothesis Testing/27047254-4.10.Hypothesis-testing-section-practical-example.xlsx 53.1 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591454-TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 51.2 kB21 - Statistics - Practical Example_ Hypothesis Testing/27047334-4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 45.3 kB21 - Statistics - Practical Example_ Hypothesis Testing/27047330-4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 44.7 kB42 - Deep Learning - Introduction to Neural Networks/17187788-GD-function-example.xlsx 43.4 kB15 - Statistics - Descriptive Statistics/13055414-2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 42.1 kB15 - Statistics - Descriptive Statistics/13055464-2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 41.4 kB15 - Statistics - Descriptive Statistics/13055492-2.8.Skewness-lesson.xlsx 35.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/15271310-Absenteeism-data.csv 32.8 kB15 - Statistics - Descriptive Statistics/13055774-2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31.5 kB11 - Probability - Bayesian Inference/18886392-Bayesian-Homework-Solutions.pdf 31.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588416-sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 30.5 kB15 - Statistics - Descriptive Statistics/13055824-2.11.Covariance-exercise-solution.xlsx 30.2 kB15 - Statistics - Descriptive Statistics/13055838-2.12.Correlation-exercise-solution.xlsx 30.2 kB15 - Statistics - Descriptive Statistics/13055834-2.12.Correlation-exercise.xlsx 30.0 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/15364076-Absenteeism-preprocessed.csv 29.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/15271330-df-preprocessed.csv 29.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588208-sklearn-Simple-Linear-Regression-with-comments.ipynb 29.0 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589824-TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.6 kB11 - Probability - Bayesian Inference/18886388-Bayesian-Homework.pdf 27.9 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591468-TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.6 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591464-TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 27.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/33130186-Simple-Linear-Regression-with-sklearn-Exercise-Solution.ipynb 27.2 kB15 - Statistics - Descriptive Statistics/13055456-2.6.Cross-table-and-scatter-plot.xlsx 26.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588206-sklearn-Simple-Linear-Regression.ipynb 26.7 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/16413674-3.9.The-z-table.xlsx 26.2 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/16413678-3.9.The-z-table.xlsx 26.2 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591442-TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 26.2 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591444-TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 26.1 kB62 - Appendix - Additional Python Tools/29535546-Additional-Python-Tools-Solutions.ipynb 26.1 kB62 - Appendix - Additional Python Tools/29535554-Additional-Python-Tools-Solutions.ipynb 26.1 kB15 - Statistics - Descriptive Statistics/13055814-2.11.Covariance-lesson.xlsx 25.5 kB17 - Statistics - Inferential Statistics Fundamentals/14171118-3.4.Standard-normal-distribution-exercise-solution.xlsx 24.6 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591432-TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 24.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588422-sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.6 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591458-TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 22.3 kB
01 - Part 1_ Introduction/003 Download All Resources and Important FAQ.html 21.9 kB16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics__en.srt 21.4 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589934-8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 21.1 kB12 - Probability - Distributions/015 A Practical Example of Probability Distributions__en.srt 20.8 kB14 - Part 3_ Statistics/15762096-Statistics-Glossary.xlsx 20.8 kB15 - Statistics - Descriptive Statistics/13055822-2.11.Covariance-exercise.xlsx 20.7 kB12 - Probability - Distributions/17971260-Daily-Views-post.xlsx 20.7 kB15 - Statistics - Descriptive Statistics/18029224-Glossary.xlsx 20.4 kB15 - Statistics - Descriptive Statistics/13055502-2.8.Skewness-exercise-solution.xlsx 20.2 kB11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference__en.srt 20.2 kB51 - Deep Learning - Business Case Example/29590002-TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 20.2 kB36 - Advanced Statistical Methods - Logistic Regression/15451889-Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/15451939-Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/15451967-Bank-data.csv 20.0 kB36 - Advanced Statistical Methods - Logistic Regression/15452033-Bank-data.csv 20.0 kB17 - Statistics - Inferential Statistics Fundamentals/13055898-3.2.What-is-a-distribution-lesson.xlsx 19.9 kB15 - Statistics - Descriptive Statistics/13055440-2.5.The-Histogram-lesson.xlsx 19.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588124-Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb 18.4 kB39 - Advanced Statistical Methods - Other Types of Clustering/29589070-Heatmaps-with-comments.ipynb 18.1 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591694-TensorFlow-MNIST-around-98-percent-accuracy.ipynb 18.1 kB15 - Statistics - Descriptive Statistics/13055790-2.5.The-Histogram-exercise-solution.xlsx 17.5 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591654-3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 17.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588412-SKLEAR-1.IPY 17.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589948-TensorFlow-MNIST-All-Exercises.ipynb 17.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588372-sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb 17.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588432-sklearn-Feature-Scaling-Exercise-Solution.ipynb 16.7 kB15 - Statistics - Descriptive Statistics/13055460-2.6.Cross-table-and-scatter-plot-exercise.xlsx 16.7 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056216-3.11.The-t-table.xlsx 16.2 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/21198408-3.11.The-t-table.xlsx 16.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589940-9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 16.2 kB12 - Probability - Distributions/17971268-Customers-Membership-post.xlsx 16.0 kB15 - Statistics - Descriptive Statistics/13055786-2.5.The-Histogram-exercise.xlsx 15.9 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591622-TensorFlow-MNIST-Exercises-All.ipynb 15.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588380-sklearn-Multiple-Linear-Regression-Exercise-Solution.ipynb 15.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589904-2.TensorFlow-MNIST-Depth-Solution.ipynb 15.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589908-3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 15.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29589056-Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.7 kB15 - Statistics - Descriptive Statistics/13055412-2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.6 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591690-9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.6 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589932-7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589928-6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589912-4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 15.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589952-TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.4 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1)__en.srt 15.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588400-sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2.ipynb 15.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591650-2.TensorFlow-MNIST-Depth-Solution.ipynb 15.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589896-1.TensorFlow-MNIST-Width-Solution.ipynb 15.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589920-5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 15.1 kB20 - Statistics - Hypothesis Testing/13737052-4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.9 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589960-TensorFlow-MNIST-complete-with-comments.ipynb 14.9 kB20 - Statistics - Hypothesis Testing/13056718-4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.7 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591812-TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591844-TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.7 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591658-4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.7 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591668-6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.6 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056252-3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.6 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591682-7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 14.5 kB10 - Probability - Combinatorics/011 A Practical Example of Combinatorics__en.srt 14.4 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591686-8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 14.4 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591642-1.TensorFlow-MNIST-Width-Solution.ipynb 14.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591632-0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.3 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591428-TensorFlow-Minimal-Example-All-Exercises.ipynb 14.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591660-5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.3 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056246-3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 14.1 kB19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics__en.srt 14.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588370-sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 14.0 kB62 - Appendix - Additional Python Tools/29535536-Additional-Python-Tools-Lectures.ipynb 13.8 kB62 - Appendix - Additional Python Tools/29535548-Additional-Python-Tools-Lectures.ipynb 13.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588068-Multiple-Linear-Regression-Exercise-Solution.ipynb 13.7 kB15 - Statistics - Descriptive Statistics/23038654-2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.5 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591550-12.9.TensorFlow-MNIST-with-comments.ipynb 13.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588342-sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.3 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589260-Minimal-example-All-Exercises.ipynb 13.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588394-SKLEAR-1.IPY 13.2 kB20 - Statistics - Hypothesis Testing/13056716-4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 13.1 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591892-TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591900-TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 13.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588358-sklearn-How-to-properly-include-p-values.ipynb 13.0 kB20 - Statistics - Hypothesis Testing/17710210-4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.9 kB15 - Statistics - Descriptive Statistics/19880123-2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.9 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589892-TensorFlow-MNIST-Part6-with-comments.ipynb 12.8 kB40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful___en.srt 12.7 kB62 - Appendix - Additional Python Tools/005 List Comprehensions__en.srt 12.6 kB62 - Appendix - Additional Python Tools/001 Using the .format() Method__en.srt 12.6 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591408-5.6.TensorFlow-Minimal-example-complete.ipynb 12.4 kB17 - Statistics - Inferential Statistics Fundamentals/14171114-3.4.Standard-normal-distribution-exercise.xlsx 12.3 kB02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI__en.srt 12.2 kB51 - Deep Learning - Business Case Example/29590012-TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB51 - Deep Learning - Business Case Example/29590020-TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.2 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4)__en.srt 12.0 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing_en.vtt 12.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588392-sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 12.0 kB51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data_en.vtt 12.0 kB36 - Advanced Statistical Methods - Logistic Regression/29588842-Accuracy-with-comments.ipynb 12.0 kB15 - Statistics - Descriptive Statistics/19880121-2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.9 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591538-12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.8 kB15 - Statistics - Descriptive Statistics/14679830-2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589236-Minimal-example-Part-4-Complete.ipynb 11.7 kB20 - Statistics - Hypothesis Testing/16190542-4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.7 kB62 - Appendix - Additional Python Tools/29535540-Additional-Python-Tools-Exercises.ipynb 11.6 kB62 - Appendix - Additional Python Tools/29535552-Additional-Python-Tools-Exercises.ipynb 11.6 kB15 - Statistics - Descriptive Statistics/13055486-2.7.Mean-median-and-mode-exercise-solution.xlsx 11.6 kB20 - Statistics - Hypothesis Testing/13056708-4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.6 kB05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods__en.srt 11.6 kB20 - Statistics - Hypothesis Testing/18041220-4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.5 kB20 - Statistics - Hypothesis Testing/13056688-4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056180-3.9.Population-variance-known-z-score-lesson.xlsx 11.5 kB51 - Deep Learning - Business Case Example/29589978-TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591738-TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591820-TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591846-TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056200-3.9.Population-variance-known-z-score-exercise-solution.xlsx 11.4 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056228-3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.4 kB15 - Statistics - Descriptive Statistics/13055520-2.9.Variance-exercise-solution.xlsx 11.3 kB20 - Statistics - Hypothesis Testing/13056684-4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.3 kB02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction__en.srt 11.3 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589888-TensorFlow-MNIST-Part5-with-comments.ipynb 11.2 kB15 - Statistics - Descriptive Statistics/13055800-2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.2 kB20 - Statistics - Hypothesis Testing/13056520-4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.2 kB15 - Statistics - Descriptive Statistics/13055484-2.7.Mean-median-and-mode-exercise.xlsx 11.1 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056196-3.9.Population-variance-known-z-score-exercise.xlsx 11.1 kB15 - Statistics - Descriptive Statistics/13055516-2.9.Variance-exercise.xlsx 11.1 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056212-3.11.Population-variance-unknown-t-score-lesson.xlsx 11.0 kB20 - Statistics - Hypothesis Testing/16200120-4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11.0 kB38 - Advanced Statistical Methods - K-Means Clustering/29589052-Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 11.0 kB05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data__en.srt 11.0 kB63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series__en.srt 10.9 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591888-TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591894-TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.9 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset__en.srt 10.9 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056226-3.11.Population-variance-unknown-t-score-exercise.xlsx 10.9 kB56 - Software Integration/003 Taking a Closer Look at APIs__en.srt 10.9 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)__en.srt 10.8 kB20 - Statistics - Hypothesis Testing/16190540-4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.8 kB51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors__en.srt 10.8 kB15 - Statistics - Descriptive Statistics/13055474-2.7.Mean-median-and-mode-lesson.xlsx 10.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589884-TensorFlow-MNIST-Part4-with-comments.ipynb 10.7 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056236-3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.7 kB05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning__en.srt 10.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588340-sklearn-Feature-Selection-with-F-regression.ipynb 10.7 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5)__en.srt 10.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588312-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.7 kB17 - Statistics - Inferential Statistics Fundamentals/13055942-3.4.Standard-normal-distribution-lesson.xlsx 10.6 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591910-TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.6 kB38 - Advanced Statistical Methods - K-Means Clustering/15452987-Categorical.csv 10.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588324-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.6 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature__en.srt 10.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score__en.srt 10.5 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau__en.srt 10.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056292-3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.4 kB15 - Statistics - Descriptive Statistics/13055510-2.9.Variance-lesson.xlsx 10.3 kB51 - Deep Learning - Business Case Example/29590006-TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence__en.srt 10.3 kB28 - Python - Sequences/001 Lists__en.srt 10.3 kB63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames__en.srt 10.3 kB51 - Deep Learning - Business Case Example/29589992-TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591948-TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning__en.srt 10.2 kB62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions__en.srt 10.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588328-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 10.1 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056280-3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 10.1 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056290-3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 10.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained__en.srt 10.1 kB13 - Probability - Probability in Other Fields/001 Probability in Finance__en.srt 10.0 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056318-3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 10.0 kB20 - Statistics - Hypothesis Testing/13056712-4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB12 - Probability - Distributions/002 Types of Probability Distributions__en.srt 10.0 kB12 - Probability - Distributions/17971264-Customers-Membership.xlsx 9.9 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau__en.srt 9.9 kB20 - Statistics - Hypothesis Testing/13056720-4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB40 - Part 6_ Mathematics/010 Dot Product of Matrices__en.srt 9.8 kB12 - Probability - Distributions/17971258-Daily-Views.xlsx 9.8 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056308-3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch__en.srt 9.7 kB15 - Statistics - Descriptive Statistics/13055500-2.8.Skewness-exercise.xlsx 9.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588142-Making-predictions-with-comments.ipynb 9.6 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591906-TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB20 - Statistics - Hypothesis Testing/13056726-4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)__en.srt 9.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/13056316-3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML__en.srt 9.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588310-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter__en.srt 9.3 kB20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level__en.srt 9.3 kB28 - Python - Sequences/005 Dictionaries__en.srt 9.3 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589782-TensorFlow-Minimal-example-Part2.ipynb 9.3 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588440-sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB09 - Part 2_ Probability/001 The Basic Probability Formula__en.srt 9.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline__en.srt 9.2 kB05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques__en.srt 9.2 kB05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques__en.srt 9.1 kB12 - Probability - Distributions/008 Characteristics of Continuous Distributions__en.srt 9.1 kB42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent__en.srt 9.0 kB21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing__en.srt 8.9 kB12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution__en.srt 8.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588246-sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591380-5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB13 - Probability - Probability in Other Fields/002 Probability in Statistics__en.srt 8.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589878-TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB51 - Deep Learning - Business Case Example/29589984-TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591944-TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression__en.srt 8.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set__en.srt 8.8 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591520-12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints___en.srt 8.7 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545334-Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29589008-How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing__en.srt 8.7 kB28 - Python - Sequences/002 Using Methods__en.srt 8.6 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing__en.srt 8.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545316-Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known__en.srt 8.5 kB62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops__en.srt 8.5 kB36 - Advanced Statistical Methods - Logistic Regression/15452035-Bank-data-testing.csv 8.5 kB38 - Advanced Statistical Methods - K-Means Clustering/15453017-Countries-exercise.csv 8.5 kB38 - Advanced Statistical Methods - K-Means Clustering/29588950-Countries-exercise.csv 8.5 kB38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering_en.vtt 8.5 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples__en.srt 8.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables__en.srt 8.4 kB63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]__en.srt 8.3 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem__en.srt 8.2 kB29 - Python - Iterations/003 Lists with the range() Function__en.srt 8.2 kB62 - Appendix - Additional Python Tools/004 Triple Nested For Loops__en.srt 8.2 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2__en.srt 8.2 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python__en.srt 8.1 kB51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism__en.srt 8.1 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column__en.srt 8.1 kB12 - Probability - Distributions/001 Fundamentals of Probability Distributions__en.srt 8.1 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591514-12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB15 - Statistics - Descriptive Statistics/015 Variance__en.srt 8.1 kB50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning__en.srt 8.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588244-sklearn-Multiple-Linear-Regression.ipynb 8.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)__en.srt 8.0 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider__en.srt 8.0 kB42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent__en.srt 7.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python__en.srt 7.9 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output__en.srt 7.9 kB29 - Python - Iterations/004 Conditional Statements and Loops__en.srt 7.9 kB22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks__en.srt 7.8 kB63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II__en.srt 7.8 kB29 - Python - Iterations/006 How to Iterate over Dictionaries__en.srt 7.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared__en.srt 7.8 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases__en.srt 7.7 kB36 - Advanced Statistical Methods - Logistic Regression/29588876-Testing-the-model-with-comments.ipynb 7.7 kB23 - Python - Variables and Data Types/29544578-Strings-Lecture-Py3.ipynb 7.7 kB11 - Probability - Bayesian Inference/011 Bayes' Law__en.srt 7.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29589000-Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights__en.srt 7.6 kB38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters__en.srt 7.6 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau__en.srt 7.5 kB38 - Advanced Statistical Methods - K-Means Clustering/29589048-Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science__en.srt 7.5 kB20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis__en.srt 7.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545314-Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)__en.srt 7.5 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591504-12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression__en.srt 7.5 kB39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram__en.srt 7.5 kB50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model__en.srt 7.4 kB28 - Python - Sequences/004 Tuples__en.srt 7.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588436-sklearn-Train-Test-Split.ipynb 7.4 kB09 - Part 2_ Probability/004 Events and Their Complements__en.srt 7.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set__en.srt 7.3 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)_en.vtt 7.3 kB23 - Python - Variables and Data Types/003 Python Strings__en.srt 7.3 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588120-Dummy-variables-with-comments.ipynb 7.3 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline__en.srt 7.3 kB63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I__en.srt 7.2 kB22 - Part 4_ Introduction to Python/001 Introduction to Programming__en.srt 7.1 kB63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I__en.srt 7.1 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)__en.srt 7.1 kB56 - Software Integration/005 Software Integration - Explained__en.srt 7.0 kB22 - Part 4_ Introduction to Python/002 Why Python___en.srt 7.0 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model__en.srt 7.0 kB38 - Advanced Statistical Methods - K-Means Clustering/29589038-Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589230-Minimal-example-Part-3.ipynb 7.0 kB46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training__en.srt 7.0 kB36 - Advanced Statistical Methods - Logistic Regression/29588894-Testing-the-Model-Exercise.ipynb 7.0 kB20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples__en.srt 6.9 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589956-TensorFlow-MNIST-complete.ipynb 6.9 kB02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many___en.srt 6.9 kB60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.vtt 6.9 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net__en.srt 6.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)__en.srt 6.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared__en.srt 6.9 kB09 - Part 2_ Probability/002 Computing Expected Values__en.srt 6.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.vtt 6.9 kB52 - Deep Learning - Conclusion/004 An overview of CNNs__en.srt 6.9 kB15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots__en.srt 6.9 kB29 - Python - Iterations/001 For Loops__en.srt 6.9 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept__en.srt 6.8 kB13 - Probability - Probability in Other Fields/003 Probability in Data Science__en.srt 6.8 kB15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation__en.srt 6.8 kB60 - Case Study - Loading the 'absenteeism_module'/29545374-absenteeism-module.py 6.8 kB50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It__en.srt 6.8 kB26 - Python - Conditional Statements/003 The ELIF Statement__en.srt 6.8 kB12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution__en.srt 6.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn__en.srt 6.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity__en.srt 6.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table__en.srt 6.7 kB04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines__en.srt 6.7 kB38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful___en.srt 6.7 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0__en.srt 6.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created__en.srt 6.7 kB38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering__en.srt 6.6 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization__en.srt 6.6 kB36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model__en.srt 6.6 kB15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques__en.srt 6.6 kB01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course__en.srt 6.6 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589876-TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy_en.vtt 6.5 kB09 - Part 2_ Probability/003 Frequency__en.srt 6.4 kB51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result__en.srt 6.4 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error__en.srt 6.4 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias__en.srt 6.4 kB63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes__en.srt 6.4 kB39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps__en.srt 6.4 kB36 - Advanced Statistical Methods - Logistic Regression/15451783-Example-bank-data.csv 6.4 kB49 - Deep Learning - Preprocessing/003 Standardization__en.srt 6.4 kB20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown__en.srt 6.3 kB37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters__en.srt 6.3 kB29 - Python - Iterations/002 While Loops and Incrementing__en.srt 6.3 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence__en.srt 6.3 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590046-5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB28 - Python - Sequences/29544994-Dictionaries-Solution-Py3.ipynb 6.3 kB15 - Statistics - Descriptive Statistics/001 Types of Data__en.srt 6.3 kB17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution__en.srt 6.3 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate__en.srt 6.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591494-12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB30 - Python - Advanced Python Tools/001 Object Oriented Programming__en.srt 6.2 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)__en.srt 6.2 kB42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks__en.srt 6.2 kB11 - Probability - Bayesian Inference/004 Union of Sets__en.srt 6.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table_en.vtt 6.2 kB38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize__en.srt 6.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588434-sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model__en.srt 6.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588166-sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects__en.srt 6.2 kB40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices__en.srt 6.2 kB15 - Statistics - Descriptive Statistics/011 Mean, median and mode__en.srt 6.1 kB25 - Python - Other Python Operators/002 Logical and Identity Operators__en.srt 6.1 kB56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses__en.srt 6.1 kB36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python__en.srt 6.0 kB38 - Advanced Statistical Methods - K-Means Clustering/29589022-Market-segmentation-example-with-comments.ipynb 6.0 kB25 - Python - Other Python Operators/29544754-Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB25 - Python - Other Python Operators/29544770-Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients__en.srt 6.0 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications__en.srt 5.9 kB38 - Advanced Statistical Methods - K-Means Clustering/29588940-Country-clusters-with-comments.ipynb 5.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588138-Making-predictions.ipynb 5.9 kB36 - Advanced Statistical Methods - Logistic Regression/29588864-Testing-the-model.ipynb 5.9 kB05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data__en.srt 5.9 kB46 - Deep Learning - Overfitting/001 What is Overfitting___en.srt 5.9 kB10 - Probability - Combinatorics/006 Solving Combinations__en.srt 5.9 kB40 - Part 6_ Mathematics/008 Transpose of a Matrix__en.srt 5.8 kB36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression__en.srt 5.8 kB17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem__en.srt 5.8 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588382-sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score__en.srt 5.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets___en.srt 5.8 kB42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss__en.srt 5.8 kB38 - Advanced Statistical Methods - K-Means Clustering/29588968-Categorical-data-with-comments.ipynb 5.8 kB14 - Part 3_ Statistics/001 Population and Sample__en.srt 5.7 kB63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()__en.srt 5.7 kB51 - Deep Learning - Business Case Example/29589970-TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591734-TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1)__en.srt 5.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29589006-How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB63 - Appendix - pandas Fundamentals/006 Using .sort_values()__en.srt 5.7 kB57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise__en.srt 5.7 kB27 - Python - Python Functions/29544926-Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables__en.srt 5.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module__en.srt 5.6 kB63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[]__en.srt 5.6 kB56 - Software Integration/004 Communication between Software Products through Text Files__en.srt 5.6 kB63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas__en.srt 5.6 kB20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)__en.srt 5.6 kB23 - Python - Variables and Data Types/29544586-Strings-Solution-Py3.ipynb 5.6 kB08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions__en.srt 5.6 kB12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution__en.srt 5.5 kB42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs__en.srt 5.5 kB12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution__en.srt 5.5 kB36 - Advanced Statistical Methods - Logistic Regression/29588854-Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB11 - Probability - Bayesian Inference/001 Sets and Events__en.srt 5.5 kB20 - Statistics - Hypothesis Testing/007 p-value__en.srt 5.5 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model__en.srt 5.5 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions__en.srt 5.5 kB36 - Advanced Statistical Methods - Logistic Regression/29588644-Admittance-with-comments.ipynb 5.4 kB42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning__en.srt 5.4 kB52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches__en.srt 5.4 kB11 - Probability - Bayesian Inference/007 The Conditional Probability Formula__en.srt 5.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity__en.srt 5.4 kB20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error__en.srt 5.4 kB52 - Deep Learning - Conclusion/001 Summary on What You've Learned__en.srt 5.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python__en.srt 5.4 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )__en.srt 5.3 kB28 - Python - Sequences/003 List Slicing__en.srt 5.3 kB02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic__en.srt 5.3 kB01 - Part 1_ Introduction/002 What Does the Course Cover__en.srt 5.2 kB36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean__en.srt 5.2 kB02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics__en.srt 5.2 kB28 - Python - Sequences/29544952-List-Slicing-Lecture-Py3.ipynb 5.1 kB17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution__en.srt 5.1 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)__en.srt 5.1 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)__en.srt 5.1 kB15 - Statistics - Descriptive Statistics/019 Covariance__en.srt 5.1 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment_en.vtt 5.1 kB12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution__en.srt 5.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588164-sklearn-Simple-Linear-Regression.ipynb 5.0 kB38 - Advanced Statistical Methods - K-Means Clustering/29588986-Clustering-Categorical-Data-Solution.ipynb 5.0 kB46 - Deep Learning - Overfitting/003 What is Validation___en.srt 5.0 kB36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function__en.srt 5.0 kB36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting__en.srt 5.0 kB60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I__en.srt 5.0 kB51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data__en.srt 4.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation__en.srt 4.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545298-Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework__en.srt 4.9 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)__en.srt 4.9 kB36 - Advanced Statistical Methods - Logistic Regression/29588700-Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding__en.srt 4.9 kB37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis__en.srt 4.9 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent__en.srt 4.9 kB15 - Statistics - Descriptive Statistics/021 Correlation Coefficient__en.srt 4.9 kB39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering__en.srt 4.9 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent__en.srt 4.9 kB10 - Probability - Combinatorics/005 Solving Variations without Repetition__en.srt 4.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation_en.vtt 4.9 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries_en.vtt 4.8 kB51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset__en.srt 4.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset__en.srt 4.8 kB15 - Statistics - Descriptive Statistics/002 Levels of Measurement__en.srt 4.8 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity__en.srt 4.8 kB38 - Advanced Statistical Methods - K-Means Clustering/29589036-Market-segmentation-example-Part2.ipynb 4.8 kB11 - Probability - Bayesian Inference/010 The Multiplication Law__en.srt 4.8 kB38 - Advanced Statistical Methods - K-Means Clustering/29588954-A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework_en.vtt 4.8 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset__en.srt 4.7 kB22 - Part 4_ Introduction to Python/003 Why Jupyter___en.srt 4.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588094-Dummy-Variables.ipynb 4.7 kB51 - Deep Learning - Business Case Example/29590000-TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB41 - Part 7_ Deep Learning/001 What to Expect from this Part___en.srt 4.7 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro_en.vtt 4.7 kB28 - Python - Sequences/29544978-Tuples-Solution-Py3.ipynb 4.7 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model_en.vtt 4.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation__en.srt 4.7 kB40 - Part 6_ Mathematics/29589122-Scalars-Vectors-and-Matrices.ipynb 4.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression__en.srt 4.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29588998-Selecting-the-number-of-clusters.ipynb 4.6 kB23 - Python - Variables and Data Types/001 Variables__en.srt 4.6 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1)__en.srt 4.6 kB27 - Python - Python Functions/29544922-Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation__en.srt 4.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588832-Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation__en.srt 4.6 kB38 - Advanced Statistical Methods - K-Means Clustering/29589044-Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB30 - Python - Advanced Python Tools/004 Importing Modules in Python__en.srt 4.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588678-Building-a-Logistic-Regression-Solution.ipynb 4.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise__en.srt 4.5 kB40 - Part 6_ Mathematics/001 What is a Matrix___en.srt 4.5 kB42 - Deep Learning - Introduction to Neural Networks/002 Training the Model__en.srt 4.5 kB36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model__en.srt 4.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column__en.srt 4.5 kB11 - Probability - Bayesian Inference/002 Ways Sets Can Interact__en.srt 4.5 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)__en.srt 4.5 kB28 - Python - Sequences/29544938-Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.5 kB15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table__en.srt 4.5 kB28 - Python - Sequences/29544988-Dictionaries-Lecture-Py3.ipynb 4.5 kB07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for__en.srt 4.4 kB40 - Part 6_ Mathematics/009 Dot Product__en.srt 4.4 kB27 - Python - Python Functions/002 How to Create a Function with a Parameter__en.srt 4.4 kB12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution__en.srt 4.4 kB27 - Python - Python Functions/007 Built-in Functions in Python__en.srt 4.4 kB46 - Deep Learning - Overfitting/005 N-Fold Cross Validation__en.srt 4.4 kB24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python__en.srt 4.4 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture__en.srt 4.4 kB28 - Python - Sequences/29544960-List-Slicing-Solution-Py3.ipynb 4.4 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution__en.srt 4.3 kB24 - Python - Basic Python Syntax/29544620-Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB10 - Probability - Combinatorics/002 Permutations and How to Use Them__en.srt 4.3 kB10 - Probability - Combinatorics/007 Symmetry of Combinations__en.srt 4.3 kB12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution__en.srt 4.3 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability__en.srt 4.3 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model__en.srt 4.3 kB35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3)__en.srt 4.3 kB37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites__en.srt 4.3 kB10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery__en.srt 4.3 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data__en.srt 4.3 kB17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution__en.srt 4.2 kB57 - Case Study - What's Next in the Course_/003 Introducing the Data Set__en.srt 4.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/29545338-Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB36 - Advanced Statistical Methods - Logistic Regression/29588660-Admittance-regression-tables-fixed-error.ipynb 4.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings__en.srt 4.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/33130182-Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.2 kB40 - Part 6_ Mathematics/003 Linear Algebra and Geometry__en.srt 4.2 kB38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering_en.vtt 4.2 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588016-Simple-linear-regression-with-comments.ipynb 4.2 kB40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices__en.srt 4.1 kB50 - Deep Learning - Classifying on the MNIST Dataset/29589868-TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version)__en.srt 4.0 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591484-12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python__en.srt 4.0 kB17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates__en.srt 4.0 kB40 - Part 6_ Mathematics/002 Scalars and Vectors__en.srt 3.9 kB47 - Deep Learning - Initialization/002 Types of Simple Initializations__en.srt 3.9 kB10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces__en.srt 3.9 kB49 - Deep Learning - Preprocessing/001 Preprocessing Introduction__en.srt 3.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn_en.vtt 3.9 kB38 - Advanced Statistical Methods - K-Means Clustering/29589020-Market-segmentation-example.ipynb 3.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS___en.srt 3.9 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose__en.srt 3.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/29587976-Simple-linear-regression.ipynb 3.9 kB23 - Python - Variables and Data Types/29544612-Variables-Solution-Py3.ipynb 3.9 kB38 - Advanced Statistical Methods - K-Means Clustering/29588982-Clustering-Categorical-Data-Exercise.ipynb 3.9 kB50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.9 kB52 - Deep Learning - Conclusion/005 An Overview of RNNs__en.srt 3.9 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2__en.srt 3.8 kB10 - Probability - Combinatorics/010 A Recap of Combinatorics__en.srt 3.8 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter__en.srt 3.8 kB57 - Case Study - What's Next in the Course_/002 The Business Task__en.srt 3.8 kB22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard__en.srt 3.8 kB40 - Part 6_ Mathematics/005 What is a Tensor___en.srt 3.8 kB47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization__en.srt 3.8 kB30 - Python - Advanced Python Tools/003 What is the Standard Library___en.srt 3.7 kB27 - Python - Python Functions/29544924-Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589218-Minimal-example-Part-2.ipynb 3.7 kB10 - Probability - Combinatorics/004 Solving Variations with Repetition__en.srt 3.7 kB15 - Statistics - Descriptive Statistics/013 Skewness__en.srt 3.7 kB36 - Advanced Statistical Methods - Logistic Regression/29588838-Accuracy.ipynb 3.7 kB38 - Advanced Statistical Methods - K-Means Clustering/15453059-iris-with-answers.csv 3.7 kB38 - Advanced Statistical Methods - K-Means Clustering/29588952-A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python__en.srt 3.7 kB27 - Python - Python Functions/005 Conditional Statements and Functions__en.srt 3.7 kB23 - Python - Variables and Data Types/29544526-Variables-Lecture-Py3.ipynb 3.7 kB40 - Part 6_ Mathematics/29589194-Dot-product-Part-2.ipynb 3.7 kB63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II__en.srt 3.7 kB47 - Deep Learning - Initialization/001 What is Initialization___en.srt 3.7 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm__en.srt 3.7 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum__en.srt 3.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588024-Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow__en.srt 3.6 kB10 - Probability - Combinatorics/003 Simple Operations with Factorials__en.srt 3.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588642-Admittance.ipynb 3.6 kB26 - Python - Conditional Statements/001 The IF Statement__en.srt 3.6 kB05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods__en.srt 3.6 kB24 - Python - Basic Python Syntax/29544616-Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset___en.srt 3.6 kB50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset__en.srt 3.6 kB11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets__en.srt 3.5 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting__en.srt 3.5 kB36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression__en.srt 3.5 kB25 - Python - Other Python Operators/29544776-Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)__en.srt 3.5 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression__en.srt 3.5 kB46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets__en.srt 3.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588130-real-estate-price-size-year-view.csv 3.5 kB11 - Probability - Bayesian Inference/008 The Law of Total Probability__en.srt 3.5 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages__en.srt 3.5 kB23 - Python - Variables and Data Types/29544572-Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590038-5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB38 - Advanced Statistical Methods - K-Means Clustering/29588960-Categorical-data.ipynb 3.4 kB37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering__en.srt 3.4 kB38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data__en.srt 3.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression__en.srt 3.4 kB38 - Advanced Statistical Methods - K-Means Clustering/29588936-Country-clusters.ipynb 3.4 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net___en.srt 3.4 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors__en.srt 3.4 kB27 - Python - Python Functions/29544866-Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.4 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals___en.srt 3.4 kB26 - Python - Conditional Statements/29544814-Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.3 kB23 - Python - Variables and Data Types/29544594-Numbers-and-Boolean-Values-Solution-Py3.ipynb 3.3 kB40 - Part 6_ Mathematics/29589134-Adding-and-subtracting-matrices.ipynb 3.3 kB42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs__en.srt 3.3 kB50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer__en.srt 3.3 kB28 - Python - Sequences/29544932-Lists-Solution-Py3.ipynb 3.3 kB40 - Part 6_ Mathematics/29589174-Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 kB36 - Advanced Statistical Methods - Logistic Regression/29588694-Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 kB36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip__en.srt 3.2 kB15 - Statistics - Descriptive Statistics/007 The Histogram__en.srt 3.2 kB26 - Python - Conditional Statements/002 The ELSE Statement__en.srt 3.2 kB24 - Python - Basic Python Syntax/29544648-Reassign-Values-Lecture-Py3.ipynb 3.2 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values__en.srt 3.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588128-Multiple-Linear-Regression-with-Dummies-Exercise.ipynb 3.1 kB50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data__en.srt 3.1 kB12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution__en.srt 3.0 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions__en.srt 3.0 kB29 - Python - Iterations/29545074-Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb 3.0 kB05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML)__en.srt 3.0 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise__en.srt 3.0 kB28 - Python - Sequences/29544992-Dictionaries-Exercise-Py3.ipynb 3.0 kB36 - Advanced Statistical Methods - Logistic Regression/29588676-Building-a-Logistic-Regression-Exercise.ipynb 3.0 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent__en.srt 3.0 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation__en.srt 3.0 kB26 - Python - Conditional Statements/004 A Note on Boolean Values__en.srt 3.0 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1_en.vtt 3.0 kB28 - Python - Sequences/29544972-Tuples-Lecture-Py3.ipynb 3.0 kB12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution__en.srt 3.0 kB40 - Part 6_ Mathematics/29589180-Tranpose-of-a-matrix.ipynb 3.0 kB27 - Python - Python Functions/003 Defining a Function in Python - Part II__en.srt 2.9 kB29 - Python - Iterations/29545120-Iterating-over-Dictionaries-Solution-Py3.ipynb 2.9 kB49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data__en.srt 2.9 kB64 - Bonus Lecture/001 Bonus Lecture_ Next Steps.html 2.9 kB12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution__en.srt 2.9 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping__en.srt 2.9 kB42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks__en.srt 2.9 kB28 - Python - Sequences/29544946-Help-Yourself-with-Methods-Solution-Py3.ipynb 2.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/005 What's Regression Analysis - a Quick Refresher.html 2.9 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588066-Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.9 kB11 - Probability - Bayesian Inference/009 The Additive Rule__en.srt 2.9 kB28 - Python - Sequences/29544956-List-Slicing-Exercise-Py3.ipynb 2.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588026-Simple-Linear-Regression-Exercise.ipynb 2.8 kB42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss__en.srt 2.8 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1__en.srt 2.8 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model__en.srt 2.8 kB28 - Python - Sequences/29544928-Lists-Lecture-Py3.ipynb 2.8 kB46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification__en.srt 2.8 kB11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets__en.srt 2.7 kB52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning__en.srt 2.7 kB38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering__en.srt 2.7 kB24 - Python - Basic Python Syntax/29544618-Arithmetic-Operators-Exercise-Py3.ipynb 2.7 kB23 - Python - Variables and Data Types/29544582-Strings-Exercise-Py3.ipynb 2.7 kB40 - Part 6_ Mathematics/007 Errors when Adding Matrices__en.srt 2.7 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test)__en.srt 2.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section__en.vtt 2.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588712-2.02.Binary-predictors.csv 2.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588826-Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.6 kB25 - Python - Other Python Operators/29544734-Comparison-Operators-Lecture-Py3.ipynb 2.6 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section__en.srt 2.6 kB12 - Probability - Distributions/003 Characteristics of Discrete Distributions__en.srt 2.6 kB55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution__en.srt 2.6 kB25 - Python - Other Python Operators/001 Comparison Operators__en.srt 2.6 kB11 - Probability - Bayesian Inference/003 Intersection of Sets__en.srt 2.5 kB36 - Advanced Statistical Methods - Logistic Regression/29588668-Admittance-regression-summary-error.ipynb 2.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588072-Multiple-Linear-Regression-Exercise.ipynb 2.5 kB45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer___en.srt 2.5 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/001 What to Expect from the Following Sections_.html 2.5 kB27 - Python - Python Functions/001 Defining a Function in Python__en.srt 2.5 kB36 - Advanced Statistical Methods - Logistic Regression/29588716-Binary-predictors.ipynb 2.5 kB25 - Python - Other Python Operators/29544744-Comparison-Operators-Solution-Py3.ipynb 2.5 kB38 - Advanced Statistical Methods - K-Means Clustering/15453029-iris-dataset.csv 2.5 kB38 - Advanced Statistical Methods - K-Means Clustering/15453055-iris-dataset.csv 2.5 kB26 - Python - Conditional Statements/29544822-Else-If-for-Brief-Elif-Solution-Py3.ipynb 2.5 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1_ Linearity__en.srt 2.4 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588076-real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588378-real-estate-price-size-year.csv 2.4 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588430-real-estate-price-size-year.csv 2.4 kB29 - Python - Iterations/005 Conditional Statements, Functions, and Loops__en.srt 2.4 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/014 Dropping a Dummy Variable from the Data Set.html 2.4 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow__en.srt 2.3 kB23 - Python - Variables and Data Types/29544590-Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/003 A Note on Installing Packages in Anaconda.html 2.3 kB29 - Python - Iterations/29545048-Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 kB38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression__en.srt 2.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages__en.srt 2.3 kB20 - Statistics - Hypothesis Testing/002 Further Reading on Null and Alternative Hypothesis.html 2.3 kB23 - Python - Variables and Data Types/29544602-Variables-Exercise-Py3.ipynb 2.3 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/011 MNIST_ Solutions.html 2.3 kB05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data__en.srt 2.3 kB31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis__en.srt 2.3 kB51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model__en.srt 2.3 kB26 - Python - Conditional Statements/29544792-Introduction-to-the-If-Statement-Solution-Py3.ipynb 2.2 kB29 - Python - Iterations/29545118-Iterating-over-Dictionaries-Exercise-Py3.ipynb 2.2 kB24 - Python - Basic Python Syntax/007 Structuring with Indentation__en.srt 2.2 kB24 - Python - Basic Python Syntax/29544694-Indexing-Elements-Solution-Py3.ipynb 2.2 kB54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/010 MNIST_ Exercises.html 2.2 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588064-Multiple-linear-regression-and-Adjusted-R-squared.ipynb 2.2 kB28 - Python - Sequences/29544930-Lists-Exercise-Py3.ipynb 2.2 kB48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized__en.srt 2.2 kB05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI)__en.srt 2.2 kB42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function___en.srt 2.2 kB40 - Part 6_ Mathematics/29589188-Dot-product.ipynb 2.2 kB24 - Python - Basic Python Syntax/29544658-Reassign-Values-Solution-Py3.ipynb 2.2 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/014 ARTICLE - A Note on 'pickling'.html 2.2 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/24453624-Absenteeism-predictions.csv 2.2 kB61 - Case Study - Analyzing the Predicted Outputs in Tableau/29545266-Absenteeism-predictions.csv 2.2 kB29 - Python - Iterations/29545070-Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb 2.1 kB36 - Advanced Statistical Methods - Logistic Regression/29588666-Admittance-regression.ipynb 2.1 kB40 - Part 6_ Mathematics/29589126-Tensors.ipynb 2.1 kB27 - Python - Python Functions/004 How to Use a Function within a Function__en.srt 2.1 kB28 - Python - Sequences/29544976-Tuples-Exercise-Py3.ipynb 2.1 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression__en.srt 2.1 kB27 - Python - Python Functions/29544874-Another-Way-to-Define-a-Function-Solution-Py3.ipynb 2.0 kB50 - Deep Learning - Classifying on the MNIST Dataset/011 MNIST - Exercises.html 2.0 kB29 - Python - Iterations/29545058-Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb 2.0 kB18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3)__en.srt 2.0 kB51 - Deep Learning - Business Case Example/002 Business Case_ Outlining the Solution__en.srt 2.0 kB17 - Statistics - Inferential Statistics Fundamentals/007 Standard error__en.srt 2.0 kB28 - Python - Sequences/29544942-Help-Yourself-with-Methods-Exercise-Py3.ipynb 2.0 kB05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data__en.srt 1.9 kB29 - Python - Iterations/29545102-All-In-Solution-Py3.ipynb 1.9 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python__en.srt 1.9 kB60 - Case Study - Loading the 'absenteeism_module'/29545382-Absenteeism-new-data.csv 1.9 kB60 - Case Study - Loading the 'absenteeism_module'/29545388-scaler 1.9 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588022-real-estate-price-size.csv 1.9 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/33130180-real-estate-price-size.csv 1.9 kB39 - Advanced Statistical Methods - Other Types of Clustering/29589066-Heatmaps.ipynb 1.9 kB29 - Python - Iterations/29545018-For-Loops-Solution-Py3.ipynb 1.8 kB24 - Python - Basic Python Syntax/004 Add Comments__en.srt 1.8 kB27 - Python - Python Functions/29544850-Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb 1.8 kB24 - Python - Basic Python Syntax/002 The Double Equality Sign__en.srt 1.8 kB26 - Python - Conditional Statements/29544796-Add-an-Else-Statement-Lecture-Py3.ipynb 1.8 kB26 - Python - Conditional Statements/29544818-Else-If-for-Brief-Elif-Exercise-Py3.ipynb 1.8 kB29 - Python - Iterations/29545032-While-Loops-and-Incrementing-Solution-Py3.ipynb 1.8 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables_ A Statistical Perspective__en.srt 1.8 kB27 - Python - Python Functions/29544920-Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb 1.8 kB49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing__en.srt 1.8 kB36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression__en.srt 1.7 kB24 - Python - Basic Python Syntax/29544656-Reassign-Values-Exercise-Py3.ipynb 1.7 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/29589774-TensorFlow-Minimal-example-Part1.ipynb 1.7 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model__en.srt 1.7 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Basic NN Example Exercises.html 1.7 kB27 - Python - Python Functions/29544910-Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb 1.7 kB24 - Python - Basic Python Syntax/006 Indexing Elements__en.srt 1.7 kB17 - Statistics - Inferential Statistics Fundamentals/001 Introduction__en.srt 1.7 kB29 - Python - Iterations/29545092-All-In-Lecture-Py3.ipynb 1.7 kB25 - Python - Other Python Operators/29544738-Comparison-Operators-Exercise-Py3.ipynb 1.6 kB27 - Python - Python Functions/29544890-0.6.4-Using-a-Function-in-another-Function-Solution-Py3.ipynb 1.6 kB27 - Python - Python Functions/29544846-Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb 1.6 kB36 - Advanced Statistical Methods - Logistic Regression/29588638-2.01.Admittance.csv 1.6 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/010 Basic NN Example with TF Exercises.html 1.6 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section___en.srt 1.6 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs__en.srt 1.6 kB26 - Python - Conditional Statements/29544788-Introduction-to-the-If-Statement-Exercise-Py3.ipynb 1.6 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries__en.srt 1.5 kB24 - Python - Basic Python Syntax/29544716-Line-Continuation-Solution-Py3.ipynb 1.5 kB24 - Python - Basic Python Syntax/29544728-Structure-Your-Code-with-Indentation-Solution-Py3.ipynb 1.5 kB29 - Python - Iterations/29545046-Create-Lists-with-the-range-Function-Exercise-Py3.ipynb 1.5 kB24 - Python - Basic Python Syntax/29544624-The-Double-Equality-Sign-Lecture-Py3.ipynb 1.5 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/004 A Note on TensorFlow 2 Syntax__en.srt 1.4 kB26 - Python - Conditional Statements/29544804-Add-an-Else-Statement-Solution-Py3.ipynb 1.4 kB24 - Python - Basic Python Syntax/003 How to Reassign Values__en.srt 1.4 kB53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro__en.srt 1.4 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy__en.srt 1.4 kB10 - Probability - Combinatorics/001 Fundamentals of Combinatorics__en.srt 1.4 kB24 - Python - Basic Python Syntax/29544684-Indexing-Elements-Exercise-Py3.ipynb 1.4 kB30 - Python - Advanced Python Tools/002 Modules and Packages__en.srt 1.4 kB29 - Python - Iterations/29545042-Create-Lists-with-the-range-Function-Lecture-Py3.ipynb 1.4 kB27 - Python - Python Functions/006 Functions Containing a Few Arguments__en.srt 1.4 kB24 - Python - Basic Python Syntax/29544682-Indexing-Elements-Lecture-Py3.ipynb 1.3 kB32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 First Regression in Python Exercise.html 1.3 kB29 - Python - Iterations/29545100-All-In-Exercise-Py3.ipynb 1.3 kB27 - Python - Python Functions/29544904-Combining-Conditional-Statements-and-Functions-Lecture-Py3.ipynb 1.3 kB44 - Deep Learning - TensorFlow 2.0_ Introduction/009 Basic NN with TensorFlow_ Exercises.html 1.3 kB29 - Python - Iterations/29545010-For-Loops-Exercise-Py3.ipynb 1.3 kB29 - Python - Iterations/29545008-For-Loops-Lecture-Py3.ipynb 1.3 kB27 - Python - Python Functions/29544868-Another-Way-to-Define-a-Function-Exercise-Py3.ipynb 1.3 kB59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment__en.srt 1.3 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588090-1.03.Dummies.csv 1.2 kB43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589208-Minimal-example-Part-1.ipynb 1.2 kB27 - Python - Python Functions/29544848-Creating-a-Function-with-a-Parameter-Exercise-Py3.ipynb 1.2 kB24 - Python - Basic Python Syntax/005 Understanding Line Continuation__en.srt 1.2 kB26 - Python - Conditional Statements/29544784-Introduction-to-the-If-Statement-Lecture-Py3.ipynb 1.2 kB24 - Python - Basic Python Syntax/29544632-The-Double-Equality-Sign-Solution-Py3.ipynb 1.2 kB58 - Case Study - Preprocessing the 'Absenteeism_data'/029 EXERCISE - Removing the _Date_ Column.html 1.2 kB24 - Python - Basic Python Syntax/29544714-Line-Continuation-Exercise-Py3.ipynb 1.2 kB29 - Python - Iterations/29545030-While-Loops-and-Incrementing-Exercise-Py3.ipynb 1.1 kB33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588058-1.02.Multiple-linear-regression.csv 1.1 kB29 - Python - Iterations/29545028-While-Loops-and-Incrementing-Lecture-Py3.ipynb 1.1 kB29 - Python - Iterations/29545116-Iterating-over-Dictionaries-Lecture-Py3.ipynb 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588240-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588306-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588320-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588334-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588350-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588366-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588388-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588398-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/29588414-1.02.Multiple-linear-regression.csv 1.1 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn__en.srt 1.1 kB27 - Python - Python Functions/29544906-Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb 1.1 kB27 - Python - Python Functions/29544888-0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb 1.1 kB52 - Deep Learning - Conclusion/003 DeepMind and Deep Learning.html 1.1 kB24 - Python - Basic Python Syntax/29544678-Add-Comments-Lecture-Py3.ipynb 1.1 kB26 - Python - Conditional Statements/29544802-Add-an-Else-Statement-Exercise-Py3.ipynb 1.0 kB60 - Case Study - Loading the 'absenteeism_module'/29545384-model 1.0 kB27 - Python - Python Functions/29544880-0.6.4-Using-a-Function-in-another-Function-Lecture-Py3.ipynb 1.0 kB34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn__en.srt 1.0 kB60 - Case Study - Loading the 'absenteeism_module'/29545348-Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb 973 Bytes60 - Case Study - Loading the 'absenteeism_module'/004 Exporting the Obtained Data Set as a _.csv.html 964 Bytes24 - Python - Basic Python Syntax/29544720-Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb 958 Bytes24 - Python - Basic Python Syntax/29544724-Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb 956 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model__en.srt 925 Bytes32 - Advanced Statistical Methods - Linear Regression with StatsModels/29587970-1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/29588160-1.01.Simple-linear-regression.csv 922 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/29588200-1.01.Simple-linear-regression.csv 922 Bytes38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering__en.srt 902 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/033 A Note on Exporting Your Data as a _.csv File.html 880 Bytes27 - Python - Python Functions/29544842-Defining-a-Function-in-Python-Lecture-Py3.ipynb 868 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/008 EXERCISE - Dropping a Column from a DataFrame in Python.html 864 Bytes35 - Advanced Statistical Methods - Practical Example_ Linear Regression/003 A Note on Multicollinearity.html 849 Bytes24 - Python - Basic Python Syntax/29544630-The-Double-Equality-Sign-Exercise-Py3.ipynb 838 Bytes26 - Python - Conditional Statements/29544828-A-Note-on-Boolean-Values-Lecture-Py3.ipynb 791 Bytes24 - Python - Basic Python Syntax/29544712-Line-Continuation-Lecture-Py3.ipynb 779 Bytes
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/external-assets-links.txt 774 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/005 A Note on Normalization.html 729 Bytes35 - Advanced Statistical Methods - Practical Example_ Linear Regression/007 Dummy Variables - Exercise.html 705 Bytes53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/001 READ ME____.html 564 Bytes45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/009 Backpropagation - A Peek into the Mathematics of Optimization.html 539 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/005 EXERCISE - Transportation Expense vs Probability.html 529 Bytes15 - Statistics - Descriptive Statistics/016 Variance Exercise.html 522 Bytes60 - Case Study - Loading the 'absenteeism_module'/001 Are You Sure You're All Set_.html 513 Bytes35 - Advanced Statistical Methods - Practical Example_ Linear Regression/009 Linear Regression - Exercise.html 497 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/022 SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 478 Bytes55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/012 Business Case_ Final Exercise.html 441 Bytes51 - Deep Learning - Business Case Example/012 Business Case_ Final Exercise.html 433 Bytes60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II__en.srt 429 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/003 EXERCISE - Reasons vs Probability.html 385 Bytes55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/005 Business Case_ Preprocessing Exercise.html 379 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/011 A Note on Calculation of P-values with sklearn.html 370 Bytes51 - Deep Learning - Business Case Example/005 Business Case_ Preprocessing the Data - Exercise.html 370 Bytes61 - Case Study - Analyzing the Predicted Outputs in Tableau/001 EXERCISE - Age vs Probability.html 367 Bytes51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data__en.srt 348 Bytes55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing__en.srt 348 Bytes36 - Advanced Statistical Methods - Logistic Regression/29588872-2.03.Test-dataset.csv 322 Bytes59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/015 EXERCISE - Saving the Model (and Scaler).html 284 Bytes38 - Advanced Statistical Methods - K-Means Clustering/29589028-3.12.Example.csv 283 Bytes39 - Advanced Statistical Methods - Other Types of Clustering/29589074-Country-clusters-standardized.csv 244 Bytes38 - Advanced Statistical Methods - K-Means Clustering/29588934-3.01.Country-clusters.csv 200 Bytes51 - Deep Learning - Business Case Example/010 Setting an Early Stopping Mechanism - Exercise.html 192 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/018 EXERCISE - Using .concat() in Python.html 189 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/021 EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 161 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/019 SOLUTION - Using .concat() in Python.html 143 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/024 EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 Bytes
0. Websites you may like/[FCS Forum].url 133 Bytes35 - Advanced Statistical Methods - Practical Example_ Linear Regression/external-assets-links.txt 130 Bytes0. Websites you may like/[FreeCourseSite.com].url 127 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/012 EXERCISE - Obtaining Dummies from a Single Feature.html 123 Bytes0. Websites you may like/[CourseClub.ME].url 122 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/025 SOLUTION - Creating Checkpoints while Coding in Jupyter.html 118 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/013 SOLUTION - Obtaining Dummies from a Single Feature.html 117 Bytes58 - Case Study - Preprocessing the 'Absenteeism_data'/009 SOLUTION - Dropping a Column from a DataFrame in Python.html 114 Bytes01 - Part 1_ Introduction/external-assets-links.txt 101 Bytes36 - Advanced Statistical Methods - Logistic Regression/005 Building a Logistic Regression - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/008 Understanding Logistic Regression Tables - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/011 Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/013 Calculating the Accuracy of the Model.html 87 Bytes36 - Advanced Statistical Methods - Logistic Regression/016 Testing the Model - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/003 A Simple Example of Clustering - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering Categorical Data - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/007 How to Choose the Number of Clusters - Exercise.html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/014 EXERCISE_ Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes38 - Advanced Statistical Methods - K-Means Clustering/015 EXERCISE_ Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes15 - Statistics - Descriptive Statistics/004 Categorical Variables Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/006 Numerical Variables Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/008 Histogram Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/010 Cross Tables and Scatter Plots Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/012 Mean, Median and Mode Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/014 Skewness Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/018 Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/020 Covariance Exercise.html 81 Bytes15 - Statistics - Descriptive Statistics/022 Correlation Coefficient Exercise.html 81 Bytes16 - Statistics - Practical Example_ Descriptive Statistics/002 Practical Example_ Descriptive Statistics Exercise.html 81 Bytes17 - Statistics - Inferential Statistics Fundamentals/005 The Standard Normal Distribution Exercise.html 81 Bytes18 - Statistics - Inferential Statistics_ Confidence Intervals/003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 Bytes18 - Statistics - Inferential Statistics_ Confidence Intervals/007 Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 Bytes18 - Statistics - Inferential Statistics_ Confidence Intervals/010 Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes18 - Statistics - Inferential Statistics_ Confidence Intervals/012 Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 Bytes18 - Statistics - Inferential Statistics_ Confidence Intervals/014 Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 Bytes19 - Statistics - Practical Example_ Inferential Statistics/002 Practical Example_ Inferential Statistics Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/006 Test for the Mean. Population Variance Known Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/009 Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/011 Test for the Mean. Dependent Samples Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/013 Test for the mean. Independent Samples (Part 1). Exercise.html 81 Bytes20 - Statistics - Hypothesis Testing/015 Test for the mean. Independent Samples (Part 2). Exercise.html 81 Bytes21 - Statistics - Practical Example_ Hypothesis Testing/002 Practical Example_ Hypothesis Testing Exercise.html 81 Bytes50 - Deep Learning - Classifying on the MNIST Dataset/005 MNIST_ Preprocess the Data - Scale the Test Data - Exercise.html 79 Bytes50 - Deep Learning - Classifying on the MNIST Dataset/007 MNIST_ Preprocess the Data - Shuffle and Batch - Exercise.html 79 Bytes51 - Deep Learning - Business Case Example/007 Business Case_ Load the Preprocessed Data - Exercise.html 79 Bytes33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple Linear Regression Exercise.html 76 Bytes33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 Dealing with Categorical Data - Dummy Variables.html 76 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/006 Simple Linear Regression with sklearn - Exercise.html 76 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/009 Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/013 Multiple Linear Regression - Exercise.html 76 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/017 Feature Scaling (Standardization) - Exercise.html 76 Bytes35 - Advanced Statistical Methods - Practical Example_ Linear Regression/005 Dummies and Variance Inflation Factor - Exercise.html 76 Bytes0. Websites you may like/[GigaCourse.Com].url 49 Bytes34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table__en.srt 0 Bytes
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