[FreeCourseSite.com] Udemy - The Data Science Course 2022 Complete Data Science Bootcamp

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[FreeCourseSite.com] Udemy - The Data Science Course 2022 Complete Data Science Bootcamp

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文件列表

  • 12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4 145.0 MB
  • 11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4 131.6 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4 110.6 MB
  • 40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful_.mp4 90.4 MB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1).mp4 89.0 MB
  • 20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp4 84.8 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp4 78.4 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing.mp4 78.0 MB
  • 51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data.mp4 77.4 MB
  • 56 - Software Integration/003 Taking a Closer Look at APIs.mp4 68.5 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp4 66.9 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp4 64.8 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp4 63.4 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset.mp4 63.2 MB
  • 56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_.mp4 61.7 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider.mp4 59.0 MB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many_.mp4 57.4 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp4 56.9 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp4 54.7 MB
  • 51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 53.9 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp4 53.8 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp4 53.8 MB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5).mp4 52.9 MB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 52.4 MB
  • 01 - Part 1_ Introduction/002 What Does the Course Cover.mp4 52.1 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp4 50.1 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp4 47.2 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp4 46.1 MB
  • 51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism.mp4 45.9 MB
  • 62 - Appendix - Additional Python Tools/005 List Comprehensions.mp4 45.3 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline.mp4 44.5 MB
  • 15 - Statistics - Descriptive Statistics/001 Types of Data.mp4 44.5 MB
  • 10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp4 44.3 MB
  • 56 - Software Integration/005 Software Integration - Explained.mp4 44.0 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp4 43.3 MB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp4 42.2 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp4 42.1 MB
  • 13 - Probability - Probability in Other Fields/001 Probability in Finance.mp4 41.6 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column.mp4 40.8 MB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp4 40.6 MB
  • 20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp4 40.1 MB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing.mp4 40.0 MB
  • 63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp4 39.1 MB
  • 16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics.mp4 39.0 MB
  • 20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp4 38.8 MB
  • 15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp4 38.4 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_.mp4 38.3 MB
  • 09 - Part 2_ Probability/003 Frequency.mp4 38.2 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp4 37.9 MB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp4 37.7 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp4 37.3 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp4 37.0 MB
  • 37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp4 36.8 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables.mp4 36.8 MB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline.mp4 36.4 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp4 36.1 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp4 35.9 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp4 35.7 MB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp4 35.6 MB
  • 62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp4 35.4 MB
  • 20 - Statistics - Hypothesis Testing/007 p-value.mp4 34.7 MB
  • 22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter.mp4 34.5 MB
  • 20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp4 34.4 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch.mp4 34.3 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression.mp4 34.1 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model.mp4 33.5 MB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2).mp4 33.5 MB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning.mp4 33.4 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp4 33.2 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp4 33.2 MB
  • 15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp4 33.0 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning.mp4 32.5 MB
  • 52 - Deep Learning - Conclusion/004 An overview of CNNs.mp4 31.9 MB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp4 31.5 MB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4).mp4 31.3 MB
  • 63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp4 31.2 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp4 31.1 MB
  • 09 - Part 2_ Probability/002 Computing Expected Values.mp4 30.7 MB
  • 09 - Part 2_ Probability/001 The Basic Probability Formula.mp4 30.5 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 30.3 MB
  • 12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp4 30.3 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp4 30.1 MB
  • 12 - Probability - Distributions/002 Types of Probability Distributions.mp4 30.1 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module.mp4 30.0 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals_.mp4 29.8 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 29.4 MB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp4 29.4 MB
  • 51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result.mp4 29.1 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp4 29.0 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity.mp4 28.7 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0.mp4 28.7 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp4 28.5 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2.mp4 28.3 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept.mp4 28.3 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization.mp4 28.3 MB
  • 40 - Part 6_ Mathematics/010 Dot Product of Matrices.mp4 27.7 MB
  • 63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp4 27.6 MB
  • 51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset.mp4 27.5 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp4 27.3 MB
  • 60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp4 27.3 MB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp4 27.0 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment.mp4 26.8 MB
  • 12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution.mp4 26.2 MB
  • 28 - Python - Sequences/005 Dictionaries.mp4 26.1 MB
  • 20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp4 25.7 MB
  • 13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp4 25.1 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp4 24.8 MB
  • 29 - Python - Iterations/001 For Loops.mp4 24.7 MB
  • 63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp4 24.7 MB
  • 28 - Python - Sequences/002 Using Methods.mp4 24.6 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp4 24.0 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp4 24.0 MB
  • 42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent.mp4 23.8 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error.mp4 23.8 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model.mp4 23.7 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp4 23.5 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_.mp4 23.5 MB
  • 63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp4 23.3 MB
  • 19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics.mp4 23.2 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model.mp4 23.2 MB
  • 40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices.mp4 23.1 MB
  • 29 - Python - Iterations/004 Conditional Statements and Loops.mp4 23.0 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp4 23.0 MB
  • 03 - 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 MB
  • 62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp4 22.7 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp4 22.6 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset.mp4 22.6 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp4 22.2 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp4 22.2 MB
  • 11 - Probability - Bayesian Inference/011 Bayes' Law.mp4 22.0 MB
  • 63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp4 21.7 MB
  • 12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution.mp4 21.7 MB
  • 28 - Python - Sequences/001 Lists.mp4 21.5 MB
  • 40 - Part 6_ Mathematics/008 Transpose of a Matrix.mp4 21.5 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp4 21.4 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp4 21.3 MB
  • 15 - Statistics - Descriptive Statistics/015 Variance.mp4 21.2 MB
  • 29 - Python - Iterations/002 While Loops and Incrementing.mp4 21.2 MB
  • 15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp4 21.1 MB
  • 11 - Probability - Bayesian Inference/010 The Multiplication Law.mp4 20.8 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp4 20.8 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp4 20.7 MB
  • 23 - Python - Variables and Data Types/003 Python Strings.mp4 20.7 MB
  • 20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp4 20.7 MB
  • 15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp4 20.7 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets_.mp4 20.6 MB
  • 12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution.mp4 20.6 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework.mp4 20.6 MB
  • 06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp4 20.5 MB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation.mp4 20.4 MB
  • 11 - Probability - Bayesian Inference/004 Union of Sets.mp4 20.4 MB
  • 62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp4 20.3 MB
  • 15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp4 20.3 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp4 20.3 MB
  • 12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp4 20.2 MB
  • 28 - Python - Sequences/003 List Slicing.mp4 20.1 MB
  • 56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp4 20.1 MB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net.mp4 20.1 MB
  • 11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp4 19.9 MB
  • 40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.9 MB
  • 25 - Python - Other Python Operators/002 Logical and Identity Operators.mp4 19.9 MB
  • 10 - Probability - Combinatorics/006 Solving Combinations.mp4 19.9 MB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation.mp4 19.5 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications.mp4 19.5 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp4 19.4 MB
  • 15 - Statistics - Descriptive Statistics/019 Covariance.mp4 19.3 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp4 19.2 MB
  • 20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp4 19.1 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp4 18.9 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp4 18.9 MB
  • 63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp4 18.7 MB
  • 15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp4 18.4 MB
  • 11 - Probability - Bayesian Inference/001 Sets and Events.mp4 18.3 MB
  • 47 - Deep Learning - Initialization/001 What is Initialization_.mp4 18.3 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp4 18.2 MB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp4 18.2 MB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output.mp4 17.9 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp4 17.9 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp4 17.7 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp4 17.7 MB
  • 63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp4 17.6 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model.mp4 17.6 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp4 17.6 MB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp4 17.5 MB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro.mp4 17.4 MB
  • 29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 17.3 MB
  • 08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 17.2 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.mp4 17.2 MB
  • 42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent.mp4 17.1 MB
  • 11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp4 17.1 MB
  • 21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing.mp4 17.1 MB
  • 42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp4 17.0 MB
  • 10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery.mp4 16.9 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp4 16.9 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp4 16.9 MB
  • 12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution.mp4 16.7 MB
  • 57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 16.6 MB
  • 12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution.mp4 16.5 MB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent.mp4 16.5 MB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp4 16.4 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp4 16.4 MB
  • 52 - 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 MB
  • 63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp4 16.2 MB
  • 22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp4 16.1 MB
  • 57 - Case Study - What's Next in the Course_/003 Introducing the Data Set.mp4 16.0 MB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp4 16.0 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression.mp4 16.0 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp4 15.9 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2.mp4 15.7 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp4 15.7 MB
  • 12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution.mp4 15.5 MB
  • 10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp4 15.5 MB
  • 12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution.mp4 15.3 MB
  • 29 - Python - Iterations/003 Lists with the range() Function.mp4 15.2 MB
  • 22 - Part 4_ Introduction to Python/001 Introduction to Programming.mp4 15.0 MB
  • 13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp4 15.0 MB
  • 26 - Python - Conditional Statements/003 The ELIF Statement.mp4 14.9 MB
  • 10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp4 14.7 MB
  • 10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp4 14.6 MB
  • 05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp4 14.6 MB
  • 51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data.mp4 14.5 MB
  • 10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp4 14.4 MB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp4 14.3 MB
  • 40 - Part 6_ Mathematics/003 Linear Algebra and Geometry.mp4 14.2 MB
  • 46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp4 14.2 MB
  • 10 - Probability - Combinatorics/007 Symmetry of Combinations.mp4 14.2 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp4 14.0 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution.mp4 14.0 MB
  • 63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp4 13.8 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp4 13.8 MB
  • 01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp4 13.7 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp4 13.7 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp4 13.5 MB
  • 10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp4 13.5 MB
  • 15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp4 13.4 MB
  • 04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 13.0 MB
  • 62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.9 MB
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  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise.mp4 12.8 MB
  • 48 - 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 MB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.6 MB
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  • 22 - Part 4_ Introduction to Python/002 Why Python_.mp4 12.3 MB
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  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp4 11.6 MB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net_.mp4 11.6 MB
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  • 02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction__en.srt 11.3 kB
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  • 38 - Advanced Statistical Methods - K-Means Clustering/29589052-Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 11.0 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data__en.srt 11.0 kB
  • 63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series__en.srt 10.9 kB
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  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/13056226-3.11.Population-variance-unknown-t-score-exercise.xlsx 10.9 kB
  • 56 - Software Integration/003 Taking a Closer Look at APIs__en.srt 10.9 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)__en.srt 10.8 kB
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  • 05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning__en.srt 10.7 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588340-sklearn-Feature-Selection-with-F-regression.ipynb 10.7 kB
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  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature__en.srt 10.5 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score__en.srt 10.5 kB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau__en.srt 10.5 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/13056292-3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.4 kB
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  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence__en.srt 10.3 kB
  • 28 - Python - Sequences/001 Lists__en.srt 10.3 kB
  • 63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames__en.srt 10.3 kB
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  • 62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions__en.srt 10.1 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588328-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 10.1 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained__en.srt 10.1 kB
  • 13 - Probability - Probability in Other Fields/001 Probability in Finance__en.srt 10.0 kB
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  • 40 - Part 6_ Mathematics/010 Dot Product of Matrices__en.srt 9.8 kB
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  • 50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch__en.srt 9.7 kB
  • 15 - Statistics - Descriptive Statistics/13055500-2.8.Skewness-exercise.xlsx 9.7 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588142-Making-predictions-with-comments.ipynb 9.6 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591906-TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB
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  • 38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)__en.srt 9.5 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/13056316-3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.4 kB
  • 03 - 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 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588310-sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.3 kB
  • 22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter__en.srt 9.3 kB
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  • 28 - Python - Sequences/005 Dictionaries__en.srt 9.3 kB
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  • 09 - Part 2_ Probability/001 The Basic Probability Formula__en.srt 9.3 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline__en.srt 9.2 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques__en.srt 9.2 kB
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  • 12 - Probability - Distributions/008 Characteristics of Continuous Distributions__en.srt 9.1 kB
  • 42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent__en.srt 9.0 kB
  • 21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing__en.srt 8.9 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588246-sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29591380-5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB
  • 13 - Probability - Probability in Other Fields/002 Probability in Statistics__en.srt 8.8 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/29589878-TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB
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  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression__en.srt 8.8 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set__en.srt 8.8 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591520-12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB
  • 56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints___en.srt 8.7 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/29545334-Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589008-How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing__en.srt 8.7 kB
  • 28 - Python - Sequences/002 Using Methods__en.srt 8.6 kB
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  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/29545316-Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB
  • 20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known__en.srt 8.5 kB
  • 62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops__en.srt 8.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/15452035-Bank-data-testing.csv 8.5 kB
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  • 38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering_en.vtt 8.5 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples__en.srt 8.4 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables__en.srt 8.4 kB
  • 63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]__en.srt 8.3 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem__en.srt 8.2 kB
  • 29 - Python - Iterations/003 Lists with the range() Function__en.srt 8.2 kB
  • 62 - Appendix - Additional Python Tools/004 Triple Nested For Loops__en.srt 8.2 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2__en.srt 8.2 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python__en.srt 8.1 kB
  • 51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism__en.srt 8.1 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column__en.srt 8.1 kB
  • 12 - Probability - Distributions/001 Fundamentals of Probability Distributions__en.srt 8.1 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591514-12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB
  • 15 - Statistics - Descriptive Statistics/015 Variance__en.srt 8.1 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning__en.srt 8.1 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588244-sklearn-Multiple-Linear-Regression.ipynb 8.0 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)__en.srt 8.0 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider__en.srt 8.0 kB
  • 42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent__en.srt 7.9 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python__en.srt 7.9 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output__en.srt 7.9 kB
  • 29 - Python - Iterations/004 Conditional Statements and Loops__en.srt 7.9 kB
  • 22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks__en.srt 7.8 kB
  • 63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II__en.srt 7.8 kB
  • 29 - Python - Iterations/006 How to Iterate over Dictionaries__en.srt 7.8 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared__en.srt 7.8 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases__en.srt 7.7 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588876-Testing-the-model-with-comments.ipynb 7.7 kB
  • 23 - Python - Variables and Data Types/29544578-Strings-Lecture-Py3.ipynb 7.7 kB
  • 11 - Probability - Bayesian Inference/011 Bayes' Law__en.srt 7.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589000-Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights__en.srt 7.6 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters__en.srt 7.6 kB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau__en.srt 7.5 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589048-Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.5 kB
  • 06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science__en.srt 7.5 kB
  • 20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis__en.srt 7.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/29545314-Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)__en.srt 7.5 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591504-12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression__en.srt 7.5 kB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram__en.srt 7.5 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model__en.srt 7.4 kB
  • 28 - Python - Sequences/004 Tuples__en.srt 7.4 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588436-sklearn-Train-Test-Split.ipynb 7.4 kB
  • 09 - Part 2_ Probability/004 Events and Their Complements__en.srt 7.3 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set__en.srt 7.3 kB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)_en.vtt 7.3 kB
  • 23 - Python - Variables and Data Types/003 Python Strings__en.srt 7.3 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588120-Dummy-variables-with-comments.ipynb 7.3 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline__en.srt 7.3 kB
  • 63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I__en.srt 7.2 kB
  • 22 - Part 4_ Introduction to Python/001 Introduction to Programming__en.srt 7.1 kB
  • 63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I__en.srt 7.1 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)__en.srt 7.1 kB
  • 56 - Software Integration/005 Software Integration - Explained__en.srt 7.0 kB
  • 22 - Part 4_ Introduction to Python/002 Why Python___en.srt 7.0 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model__en.srt 7.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589038-Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589230-Minimal-example-Part-3.ipynb 7.0 kB
  • 46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training__en.srt 7.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588894-Testing-the-Model-Exercise.ipynb 7.0 kB
  • 20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples__en.srt 6.9 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/29589956-TensorFlow-MNIST-complete.ipynb 6.9 kB
  • 02 - 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 kB
  • 60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.vtt 6.9 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net__en.srt 6.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)__en.srt 6.9 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared__en.srt 6.9 kB
  • 09 - Part 2_ Probability/002 Computing Expected Values__en.srt 6.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.vtt 6.9 kB
  • 52 - Deep Learning - Conclusion/004 An overview of CNNs__en.srt 6.9 kB
  • 15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots__en.srt 6.9 kB
  • 29 - Python - Iterations/001 For Loops__en.srt 6.9 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept__en.srt 6.8 kB
  • 13 - Probability - Probability in Other Fields/003 Probability in Data Science__en.srt 6.8 kB
  • 15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation__en.srt 6.8 kB
  • 60 - Case Study - Loading the 'absenteeism_module'/29545374-absenteeism-module.py 6.8 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It__en.srt 6.8 kB
  • 26 - Python - Conditional Statements/003 The ELIF Statement__en.srt 6.8 kB
  • 12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution__en.srt 6.8 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn__en.srt 6.8 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity__en.srt 6.7 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table__en.srt 6.7 kB
  • 04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines__en.srt 6.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful___en.srt 6.7 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0__en.srt 6.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created__en.srt 6.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering__en.srt 6.6 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization__en.srt 6.6 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model__en.srt 6.6 kB
  • 15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques__en.srt 6.6 kB
  • 01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course__en.srt 6.6 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/29589876-TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy_en.vtt 6.5 kB
  • 09 - Part 2_ Probability/003 Frequency__en.srt 6.4 kB
  • 51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result__en.srt 6.4 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error__en.srt 6.4 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias__en.srt 6.4 kB
  • 63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes__en.srt 6.4 kB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps__en.srt 6.4 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/15451783-Example-bank-data.csv 6.4 kB
  • 49 - Deep Learning - Preprocessing/003 Standardization__en.srt 6.4 kB
  • 20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown__en.srt 6.3 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters__en.srt 6.3 kB
  • 29 - Python - Iterations/002 While Loops and Incrementing__en.srt 6.3 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence__en.srt 6.3 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590046-5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB
  • 28 - Python - Sequences/29544994-Dictionaries-Solution-Py3.ipynb 6.3 kB
  • 15 - Statistics - Descriptive Statistics/001 Types of Data__en.srt 6.3 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution__en.srt 6.3 kB
  • 48 - 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 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591494-12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB
  • 30 - Python - Advanced Python Tools/001 Object Oriented Programming__en.srt 6.2 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)__en.srt 6.2 kB
  • 42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks__en.srt 6.2 kB
  • 11 - Probability - Bayesian Inference/004 Union of Sets__en.srt 6.2 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table_en.vtt 6.2 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize__en.srt 6.2 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588434-sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model__en.srt 6.2 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588166-sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB
  • 62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects__en.srt 6.2 kB
  • 40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices__en.srt 6.2 kB
  • 15 - Statistics - Descriptive Statistics/011 Mean, median and mode__en.srt 6.1 kB
  • 25 - Python - Other Python Operators/002 Logical and Identity Operators__en.srt 6.1 kB
  • 56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses__en.srt 6.1 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python__en.srt 6.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589022-Market-segmentation-example-with-comments.ipynb 6.0 kB
  • 25 - Python - Other Python Operators/29544754-Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB
  • 25 - Python - Other Python Operators/29544770-Logical-and-Identity-Operators-Lecture-Py3.ipynb 6.0 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients__en.srt 6.0 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications__en.srt 5.9 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588940-Country-clusters-with-comments.ipynb 5.9 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588138-Making-predictions.ipynb 5.9 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588864-Testing-the-model.ipynb 5.9 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data__en.srt 5.9 kB
  • 46 - Deep Learning - Overfitting/001 What is Overfitting___en.srt 5.9 kB
  • 10 - Probability - Combinatorics/006 Solving Combinations__en.srt 5.9 kB
  • 40 - Part 6_ Mathematics/008 Transpose of a Matrix__en.srt 5.8 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression__en.srt 5.8 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem__en.srt 5.8 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588382-sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score__en.srt 5.8 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets___en.srt 5.8 kB
  • 42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss__en.srt 5.8 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588968-Categorical-data-with-comments.ipynb 5.8 kB
  • 14 - Part 3_ Statistics/001 Population and Sample__en.srt 5.7 kB
  • 63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()__en.srt 5.7 kB
  • 51 - Deep Learning - Business Case Example/29589970-TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/29591734-TensorFlow-Audiobooks-Preprocessing.ipynb 5.7 kB
  • 20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1)__en.srt 5.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589006-How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.7 kB
  • 63 - Appendix - pandas Fundamentals/006 Using .sort_values()__en.srt 5.7 kB
  • 57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise__en.srt 5.7 kB
  • 27 - Python - Python Functions/29544926-Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.7 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables__en.srt 5.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module__en.srt 5.6 kB
  • 63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[]__en.srt 5.6 kB
  • 56 - Software Integration/004 Communication between Software Products through Text Files__en.srt 5.6 kB
  • 63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas__en.srt 5.6 kB
  • 20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)__en.srt 5.6 kB
  • 23 - Python - Variables and Data Types/29544586-Strings-Solution-Py3.ipynb 5.6 kB
  • 08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions__en.srt 5.6 kB
  • 12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution__en.srt 5.5 kB
  • 42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs__en.srt 5.5 kB
  • 12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution__en.srt 5.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588854-Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB
  • 11 - Probability - Bayesian Inference/001 Sets and Events__en.srt 5.5 kB
  • 20 - Statistics - Hypothesis Testing/007 p-value__en.srt 5.5 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model__en.srt 5.5 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions__en.srt 5.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588644-Admittance-with-comments.ipynb 5.4 kB
  • 42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning__en.srt 5.4 kB
  • 52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches__en.srt 5.4 kB
  • 11 - Probability - Bayesian Inference/007 The Conditional Probability Formula__en.srt 5.4 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity__en.srt 5.4 kB
  • 20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error__en.srt 5.4 kB
  • 52 - Deep Learning - Conclusion/001 Summary on What You've Learned__en.srt 5.4 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python__en.srt 5.4 kB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )__en.srt 5.3 kB
  • 28 - Python - Sequences/003 List Slicing__en.srt 5.3 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic__en.srt 5.3 kB
  • 01 - Part 1_ Introduction/002 What Does the Course Cover__en.srt 5.2 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean__en.srt 5.2 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics__en.srt 5.2 kB
  • 28 - Python - Sequences/29544952-List-Slicing-Lecture-Py3.ipynb 5.1 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution__en.srt 5.1 kB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2)__en.srt 5.1 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)__en.srt 5.1 kB
  • 15 - Statistics - Descriptive Statistics/019 Covariance__en.srt 5.1 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment_en.vtt 5.1 kB
  • 12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution__en.srt 5.0 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588164-sklearn-Simple-Linear-Regression.ipynb 5.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588986-Clustering-Categorical-Data-Solution.ipynb 5.0 kB
  • 46 - Deep Learning - Overfitting/003 What is Validation___en.srt 5.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function__en.srt 5.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting__en.srt 5.0 kB
  • 60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I__en.srt 5.0 kB
  • 51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data__en.srt 4.9 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation__en.srt 4.9 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/29545298-Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.9 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework__en.srt 4.9 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)__en.srt 4.9 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588700-Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB
  • 49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding__en.srt 4.9 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis__en.srt 4.9 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent__en.srt 4.9 kB
  • 15 - Statistics - Descriptive Statistics/021 Correlation Coefficient__en.srt 4.9 kB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering__en.srt 4.9 kB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent__en.srt 4.9 kB
  • 10 - Probability - Combinatorics/005 Solving Variations without Repetition__en.srt 4.9 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation_en.vtt 4.9 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries_en.vtt 4.8 kB
  • 51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset__en.srt 4.8 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset__en.srt 4.8 kB
  • 15 - Statistics - Descriptive Statistics/002 Levels of Measurement__en.srt 4.8 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity__en.srt 4.8 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589036-Market-segmentation-example-Part2.ipynb 4.8 kB
  • 11 - Probability - Bayesian Inference/010 The Multiplication Law__en.srt 4.8 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588954-A-Simple-Example-of-Clustering-Solution.ipynb 4.8 kB
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework_en.vtt 4.8 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset__en.srt 4.7 kB
  • 22 - Part 4_ Introduction to Python/003 Why Jupyter___en.srt 4.7 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588094-Dummy-Variables.ipynb 4.7 kB
  • 51 - Deep Learning - Business Case Example/29590000-TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.7 kB
  • 41 - Part 7_ Deep Learning/001 What to Expect from this Part___en.srt 4.7 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro_en.vtt 4.7 kB
  • 28 - Python - Sequences/29544978-Tuples-Solution-Py3.ipynb 4.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model_en.vtt 4.7 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation__en.srt 4.7 kB
  • 40 - Part 6_ Mathematics/29589122-Scalars-Vectors-and-Matrices.ipynb 4.7 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression__en.srt 4.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588998-Selecting-the-number-of-clusters.ipynb 4.6 kB
  • 23 - Python - Variables and Data Types/001 Variables__en.srt 4.6 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1)__en.srt 4.6 kB
  • 27 - Python - Python Functions/29544922-Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation__en.srt 4.6 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588832-Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.6 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation__en.srt 4.6 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589044-Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.6 kB
  • 30 - Python - Advanced Python Tools/004 Importing Modules in Python__en.srt 4.6 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588678-Building-a-Logistic-Regression-Solution.ipynb 4.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise__en.srt 4.5 kB
  • 40 - Part 6_ Mathematics/001 What is a Matrix___en.srt 4.5 kB
  • 42 - Deep Learning - Introduction to Neural Networks/002 Training the Model__en.srt 4.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model__en.srt 4.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column__en.srt 4.5 kB
  • 11 - Probability - Bayesian Inference/002 Ways Sets Can Interact__en.srt 4.5 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)__en.srt 4.5 kB
  • 28 - Python - Sequences/29544938-Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.5 kB
  • 15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table__en.srt 4.5 kB
  • 28 - Python - Sequences/29544988-Dictionaries-Lecture-Py3.ipynb 4.5 kB
  • 07 - 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 kB
  • 40 - Part 6_ Mathematics/009 Dot Product__en.srt 4.4 kB
  • 27 - Python - Python Functions/002 How to Create a Function with a Parameter__en.srt 4.4 kB
  • 12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution__en.srt 4.4 kB
  • 27 - Python - Python Functions/007 Built-in Functions in Python__en.srt 4.4 kB
  • 46 - Deep Learning - Overfitting/005 N-Fold Cross Validation__en.srt 4.4 kB
  • 24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python__en.srt 4.4 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture__en.srt 4.4 kB
  • 28 - Python - Sequences/29544960-List-Slicing-Solution-Py3.ipynb 4.4 kB
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution__en.srt 4.3 kB
  • 24 - Python - Basic Python Syntax/29544620-Arithmetic-Operators-Solution-Py3.ipynb 4.3 kB
  • 10 - Probability - Combinatorics/002 Permutations and How to Use Them__en.srt 4.3 kB
  • 10 - Probability - Combinatorics/007 Symmetry of Combinations__en.srt 4.3 kB
  • 12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution__en.srt 4.3 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability__en.srt 4.3 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model__en.srt 4.3 kB
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3)__en.srt 4.3 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites__en.srt 4.3 kB
  • 10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery__en.srt 4.3 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data__en.srt 4.3 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution__en.srt 4.2 kB
  • 57 - Case Study - What's Next in the Course_/003 Introducing the Data Set__en.srt 4.2 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/29545338-Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.2 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588660-Admittance-regression-tables-fixed-error.ipynb 4.2 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings__en.srt 4.2 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/33130182-Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.2 kB
  • 40 - Part 6_ Mathematics/003 Linear Algebra and Geometry__en.srt 4.2 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering_en.vtt 4.2 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588016-Simple-linear-regression-with-comments.ipynb 4.2 kB
  • 40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices__en.srt 4.1 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/29589868-TensorFlow-MNIST-Part1-with-comments.ipynb 4.1 kB
  • 42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version)__en.srt 4.0 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/29591484-12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 4.0 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python__en.srt 4.0 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates__en.srt 4.0 kB
  • 40 - Part 6_ Mathematics/002 Scalars and Vectors__en.srt 3.9 kB
  • 47 - Deep Learning - Initialization/002 Types of Simple Initializations__en.srt 3.9 kB
  • 10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces__en.srt 3.9 kB
  • 49 - Deep Learning - Preprocessing/001 Preprocessing Introduction__en.srt 3.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn_en.vtt 3.9 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589020-Market-segmentation-example.ipynb 3.9 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS___en.srt 3.9 kB
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose__en.srt 3.9 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/29587976-Simple-linear-regression.ipynb 3.9 kB
  • 23 - Python - Variables and Data Types/29544612-Variables-Solution-Py3.ipynb 3.9 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588982-Clustering-Categorical-Data-Exercise.ipynb 3.9 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.9 kB
  • 52 - Deep Learning - Conclusion/005 An Overview of RNNs__en.srt 3.9 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2__en.srt 3.8 kB
  • 10 - Probability - Combinatorics/010 A Recap of Combinatorics__en.srt 3.8 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST__en.srt 3.8 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter__en.srt 3.8 kB
  • 57 - Case Study - What's Next in the Course_/002 The Business Task__en.srt 3.8 kB
  • 22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard__en.srt 3.8 kB
  • 40 - Part 6_ Mathematics/005 What is a Tensor___en.srt 3.8 kB
  • 47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization__en.srt 3.8 kB
  • 30 - Python - Advanced Python Tools/003 What is the Standard Library___en.srt 3.7 kB
  • 27 - Python - Python Functions/29544924-Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/29589218-Minimal-example-Part-2.ipynb 3.7 kB
  • 10 - Probability - Combinatorics/004 Solving Variations with Repetition__en.srt 3.7 kB
  • 15 - Statistics - Descriptive Statistics/013 Skewness__en.srt 3.7 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588838-Accuracy.ipynb 3.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/15453059-iris-with-answers.csv 3.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588952-A-Simple-Example-of-Clustering-Exercise.ipynb 3.7 kB
  • 23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python__en.srt 3.7 kB
  • 27 - Python - Python Functions/005 Conditional Statements and Functions__en.srt 3.7 kB
  • 23 - Python - Variables and Data Types/29544526-Variables-Lecture-Py3.ipynb 3.7 kB
  • 40 - Part 6_ Mathematics/29589194-Dot-product-Part-2.ipynb 3.7 kB
  • 63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II__en.srt 3.7 kB
  • 47 - Deep Learning - Initialization/001 What is Initialization___en.srt 3.7 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm__en.srt 3.7 kB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum__en.srt 3.7 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/29588024-Simple-Linear-Regression-Exercise-Solution.ipynb 3.7 kB
  • 44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow__en.srt 3.6 kB
  • 10 - Probability - Combinatorics/003 Simple Operations with Factorials__en.srt 3.6 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/29588642-Admittance.ipynb 3.6 kB
  • 26 - Python - Conditional Statements/001 The IF Statement__en.srt 3.6 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods__en.srt 3.6 kB
  • 24 - Python - Basic Python Syntax/29544616-Arithmetic-Operators-Lecture-Py3.ipynb 3.6 kB
  • 54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset___en.srt 3.6 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset__en.srt 3.6 kB
  • 11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets__en.srt 3.5 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting__en.srt 3.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression__en.srt 3.5 kB
  • 25 - Python - Other Python Operators/29544776-Logical-and-Identity-Operators-Solution-Py3.ipynb 3.5 kB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)__en.srt 3.5 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression__en.srt 3.5 kB
  • 46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets__en.srt 3.5 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/29588130-real-estate-price-size-year-view.csv 3.5 kB
  • 11 - Probability - Bayesian Inference/008 The Law of Total Probability__en.srt 3.5 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages__en.srt 3.5 kB
  • 23 - Python - Variables and Data Types/29544572-Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 kB
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/29590038-5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 kB
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  • 60 - Case Study - Loading the 'absenteeism_module'/004 Exporting the Obtained Data Set as a _.csv.html 964 Bytes
  • 24 - Python - Basic Python Syntax/29544720-Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb 958 Bytes
  • 24 - Python - Basic Python Syntax/29544724-Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb 956 Bytes
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model__en.srt 925 Bytes
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/29587970-1.01.Simple-linear-regression.csv 922 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588160-1.01.Simple-linear-regression.csv 922 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/29588200-1.01.Simple-linear-regression.csv 922 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering__en.srt 902 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/033 A Note on Exporting Your Data as a _.csv File.html 880 Bytes
  • 27 - Python - Python Functions/29544842-Defining-a-Function-in-Python-Lecture-Py3.ipynb 868 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/008 EXERCISE - Dropping a Column from a DataFrame in Python.html 864 Bytes
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/003 A Note on Multicollinearity.html 849 Bytes
  • 24 - Python - Basic Python Syntax/29544630-The-Double-Equality-Sign-Exercise-Py3.ipynb 838 Bytes
  • 26 - Python - Conditional Statements/29544828-A-Note-on-Boolean-Values-Lecture-Py3.ipynb 791 Bytes
  • 24 - 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 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/005 A Note on Normalization.html 729 Bytes
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/007 Dummy Variables - Exercise.html 705 Bytes
  • 53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/001 READ ME____.html 564 Bytes
  • 45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/009 Backpropagation - A Peek into the Mathematics of Optimization.html 539 Bytes
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/005 EXERCISE - Transportation Expense vs Probability.html 529 Bytes
  • 15 - Statistics - Descriptive Statistics/016 Variance Exercise.html 522 Bytes
  • 60 - Case Study - Loading the 'absenteeism_module'/001 Are You Sure You're All Set_.html 513 Bytes
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/009 Linear Regression - Exercise.html 497 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/022 SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 478 Bytes
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/012 Business Case_ Final Exercise.html 441 Bytes
  • 51 - Deep Learning - Business Case Example/012 Business Case_ Final Exercise.html 433 Bytes
  • 60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II__en.srt 429 Bytes
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/003 EXERCISE - Reasons vs Probability.html 385 Bytes
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/005 Business Case_ Preprocessing Exercise.html 379 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/011 A Note on Calculation of P-values with sklearn.html 370 Bytes
  • 51 - Deep Learning - Business Case Example/005 Business Case_ Preprocessing the Data - Exercise.html 370 Bytes
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/001 EXERCISE - Age vs Probability.html 367 Bytes
  • 51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data__en.srt 348 Bytes
  • 55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing__en.srt 348 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/29588872-2.03.Test-dataset.csv 322 Bytes
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/015 EXERCISE - Saving the Model (and Scaler).html 284 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/29589028-3.12.Example.csv 283 Bytes
  • 39 - Advanced Statistical Methods - Other Types of Clustering/29589074-Country-clusters-standardized.csv 244 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/29588934-3.01.Country-clusters.csv 200 Bytes
  • 51 - Deep Learning - Business Case Example/010 Setting an Early Stopping Mechanism - Exercise.html 192 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/018 EXERCISE - Using .concat() in Python.html 189 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/021 EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 161 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/019 SOLUTION - Using .concat() in Python.html 143 Bytes
  • 58 - 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 Bytes
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/external-assets-links.txt 130 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/012 EXERCISE - Obtaining Dummies from a Single Feature.html 123 Bytes
  • 0. Websites you may like/[CourseClub.ME].url 122 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/025 SOLUTION - Creating Checkpoints while Coding in Jupyter.html 118 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/013 SOLUTION - Obtaining Dummies from a Single Feature.html 117 Bytes
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/009 SOLUTION - Dropping a Column from a DataFrame in Python.html 114 Bytes
  • 01 - Part 1_ Introduction/external-assets-links.txt 101 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/005 Building a Logistic Regression - Exercise.html 87 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/008 Understanding Logistic Regression Tables - Exercise.html 87 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/011 Binary Predictors in a Logistic Regression - Exercise.html 87 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/013 Calculating the Accuracy of the Model.html 87 Bytes
  • 36 - Advanced Statistical Methods - Logistic Regression/016 Testing the Model - Exercise.html 87 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/003 A Simple Example of Clustering - Exercise.html 87 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering Categorical Data - Exercise.html 87 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/007 How to Choose the Number of Clusters - Exercise.html 87 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/014 EXERCISE_ Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes
  • 38 - Advanced Statistical Methods - K-Means Clustering/015 EXERCISE_ Species Segmentation with Cluster Analysis (Part 2).html 87 Bytes
  • 15 - Statistics - Descriptive Statistics/004 Categorical Variables Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/006 Numerical Variables Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/008 Histogram Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/010 Cross Tables and Scatter Plots Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/012 Mean, Median and Mode Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/014 Skewness Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/018 Standard Deviation and Coefficient of Variation Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/020 Covariance Exercise.html 81 Bytes
  • 15 - Statistics - Descriptive Statistics/022 Correlation Coefficient Exercise.html 81 Bytes
  • 16 - Statistics - Practical Example_ Descriptive Statistics/002 Practical Example_ Descriptive Statistics Exercise.html 81 Bytes
  • 17 - Statistics - Inferential Statistics Fundamentals/005 The Standard Normal Distribution Exercise.html 81 Bytes
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 Bytes
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/007 Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 Bytes
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/010 Confidence intervals. Two means. Dependent samples Exercise.html 81 Bytes
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/012 Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 Bytes
  • 18 - Statistics - Inferential Statistics_ Confidence Intervals/014 Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 Bytes
  • 19 - Statistics - Practical Example_ Inferential Statistics/002 Practical Example_ Inferential Statistics Exercise.html 81 Bytes
  • 20 - Statistics - Hypothesis Testing/006 Test for the Mean. Population Variance Known Exercise.html 81 Bytes
  • 20 - Statistics - Hypothesis Testing/009 Test for the Mean. Population Variance Unknown Exercise.html 81 Bytes
  • 20 - Statistics - Hypothesis Testing/011 Test for the Mean. Dependent Samples Exercise.html 81 Bytes
  • 20 - Statistics - Hypothesis Testing/013 Test for the mean. Independent Samples (Part 1). Exercise.html 81 Bytes
  • 20 - Statistics - Hypothesis Testing/015 Test for the mean. Independent Samples (Part 2). Exercise.html 81 Bytes
  • 21 - Statistics - Practical Example_ Hypothesis Testing/002 Practical Example_ Hypothesis Testing Exercise.html 81 Bytes
  • 50 - Deep Learning - Classifying on the MNIST Dataset/005 MNIST_ Preprocess the Data - Scale the Test Data - Exercise.html 79 Bytes
  • 50 - Deep Learning - Classifying on the MNIST Dataset/007 MNIST_ Preprocess the Data - Shuffle and Batch - Exercise.html 79 Bytes
  • 51 - Deep Learning - Business Case Example/007 Business Case_ Load the Preprocessed Data - Exercise.html 79 Bytes
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple Linear Regression Exercise.html 76 Bytes
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 Dealing with Categorical Data - Dummy Variables.html 76 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/006 Simple Linear Regression with sklearn - Exercise.html 76 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/009 Calculating the Adjusted R-Squared in sklearn - Exercise.html 76 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/013 Multiple Linear Regression - Exercise.html 76 Bytes
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/017 Feature Scaling (Standardization) - Exercise.html 76 Bytes
  • 35 - Advanced Statistical Methods - Practical Example_ Linear Regression/005 Dummies and Variance Inflation Factor - Exercise.html 76 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 34 - 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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