[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

  • 16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics.mp4 157.5 MB
  • 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
  • 03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 86.0 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
  • 19 - Statistics - Practical Example Inferential Statistics/001 Practical Example Inferential Statistics.mp4 72.4 MB
  • 06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp4 70.0 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
  • 08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 60.7 MB
  • code.zip 60.0 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
  • 04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 48.1 MB
  • 21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing.mp4 48.1 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
  • 01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp4 46.0 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
  • 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
  • 14 - Part 3 Statistics/001 Population and Sample.mp4 35.8 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
  • 07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp4 34.7 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
  • 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
  • 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
  • 09 - Part 2 Probability/004 Events and Their Complements.mp4 21.8 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
  • 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
  • 13 - Probability - Probability in Other Fields/002 Probability in Statistics.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
  • 56 - Software Integration/004 Communication between Software Products through Text Files.mp4 18.1 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
  • 60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp4 17.7 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
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3).mp4 17.5 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
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp4 17.3 MB
  • 29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 17.3 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
  • 28 - Python - Sequences/004 Tuples.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/001 365-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
  • 20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).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
  • 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
  • 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
  • 05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp4 13.3 MB
  • 62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.9 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST Importing the Relevant Packages and Loading the Data.mp4 12.8 MB
  • 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
  • 10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp4 12.6 MB
  • 11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp4 12.6 MB
  • 49 - Deep Learning - Preprocessing/003 Standardization.mp4 12.5 MB
  • 22 - Part 4 Introduction to Python/002 Why Python.mp4 12.3 MB
  • 40 - Part 6 Mathematics/001 What is a Matrix.mp4 12.3 MB
  • 40 - Part 6 Mathematics/005 What is a Tensor.mp4 12.2 MB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp4 12.1 MB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm.mp4 12.1 MB
  • 11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp4 11.9 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp4 11.9 MB
  • 40 - Part 6 Mathematics/009 Dot Product.mp4 11.9 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp4 11.6 MB
  • 57 - Case Study - What's Next in the Course/002 The Business Task.mp4 11.6 MB
  • 62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.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
  • 02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp4 11.6 MB
  • 12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution.mp4 11.5 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp4 11.5 MB
  • 11 - Probability - Bayesian Inference/009 The Additive Rule.mp4 11.4 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp4 11.3 MB
  • 37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp4 11.2 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer.mp4 11.2 MB
  • 63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp4 11.1 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp4 11.0 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp4 11.0 MB
  • 46 - Deep Learning - Overfitting/001 What is Overfitting.mp4 11.0 MB
  • 42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp4 10.9 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp4 10.8 MB
  • 12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution.mp4 10.6 MB
  • 15 - Statistics - Descriptive Statistics/013 Skewness.mp4 10.4 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp4 10.4 MB
  • 42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp4 10.3 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp4 10.3 MB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression.mp4 10.3 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp4 10.2 MB
  • 42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss.mp4 10.2 MB
  • 52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp4 10.1 MB
  • 15 - Statistics - Descriptive Statistics/007 The Histogram.mp4 10.1 MB
  • 37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp4 10.0 MB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp4 9.8 MB
  • 41 - Part 7 Deep Learning/001 What to Expect from this Part.mp4 9.7 MB
  • 12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp4 9.7 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column.mp4 9.6 MB
  • 12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution.mp4 9.5 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity.mp4 9.4 MB
  • 63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp4 9.4 MB
  • 49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp4 9.4 MB
  • 23 - Python - Variables and Data Types/001 Variables.mp4 9.4 MB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp4 9.3 MB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors.mp4 9.3 MB
  • 11 - Probability - Bayesian Inference/003 Intersection of Sets.mp4 9.2 MB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping.mp4 9.1 MB
  • 12 - Probability - Distributions/015 FIFA19-post.csv 9.1 MB
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  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp4 9.0 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp4 9.0 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp4 9.0 MB
  • 11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp4 9.0 MB
  • 30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp4 8.9 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions.mp4 8.9 MB
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  • 46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp4 8.9 MB
  • 30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp4 8.8 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation.mp4 8.8 MB
  • 40 - Part 6 Mathematics/002 Scalars and Vectors.mp4 8.8 MB
  • 49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp4 8.8 MB
  • 27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp4 8.7 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.6 MB
  • 51 - Deep Learning - Business Case Example/011 Business Case Testing the Model.mp4 8.6 MB
  • 46 - Deep Learning - Overfitting/003 What is Validation.mp4 8.5 MB
  • 22 - Part 4 Introduction to Python/003 Why Jupyter.mp4 8.4 MB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages.mp4 8.3 MB
  • 42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp4 8.3 MB
  • 42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp4 8.1 MB
  • 46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp4 8.1 MB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 8.1 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture.mp4 8.1 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation.mp4 8.0 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 8.0 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp4 8.0 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 8.0 MB
  • 42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp4 7.9 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp4 7.7 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity.mp4 7.7 MB
  • 24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp4 7.6 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp4 7.6 MB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow.mp4 7.6 MB
  • 58 - 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/004 365-DataScience.png 7.3 MB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/005 365-DataScience.png 7.3 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp4 7.2 MB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp4 7.2 MB
  • 52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp4 7.1 MB
  • 23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp4 6.9 MB
  • 40 - Part 6 Mathematics/007 Errors when Adding Matrices.mp4 6.8 MB
  • 27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp4 6.8 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp4 6.8 MB
  • 42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp4 6.7 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.mp4 6.5 MB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow.mp4 6.5 MB
  • 22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 6.4 MB
  • 27 - Python - Python Functions/005 Conditional Statements and Functions.mp4 6.3 MB
  • 42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function.mp4 6.3 MB
  • 46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp4 6.3 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp4 6.2 MB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables A Statistical Perspective.mp4 6.1 MB
  • 47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp4 6.0 MB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp4 6.0 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp4 5.8 MB
  • 42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss.mp4 5.7 MB
  • 49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp4 5.6 MB
  • 26 - Python - Conditional Statements/001 The IF Statement.mp4 5.6 MB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section.mp4 5.5 MB
  • 47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.5 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp4 5.4 MB
  • 37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp4 5.3 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp4 5.2 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer.mp4 5.2 MB
  • 30 - Python - Advanced Python Tools/003 What is the Standard Library.mp4 5.1 MB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1.mp4 5.1 MB
  • 52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp4 5.0 MB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset.mp4 4.9 MB
  • 10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp4 4.9 MB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset.mp4 4.6 MB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model.mp4 4.6 MB
  • 29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp4 4.5 MB
  • 26 - Python - Conditional Statements/004 A Note on Boolean Values.mp4 4.4 MB
  • 36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp4 4.4 MB
  • 25 - Python - Other Python Operators/001 Comparison Operators.mp4 4.4 MB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution.mp4 4.2 MB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp4 3.9 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp4 3.7 MB
  • 31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp4 3.7 MB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1 Linearity.mp4 3.6 MB
  • 38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp4 3.6 MB
  • 27 - Python - Python Functions/004 How to Use a Function within a Function.mp4 3.4 MB
  • 27 - Python - Python Functions/001 Defining a Function in Python.mp4 3.4 MB
  • 49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp4 3.3 MB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp4 3.2 MB
  • 17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp4 3.2 MB
  • 51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution.mp4 3.1 MB
  • 27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp4 3.0 MB
  • 24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp4 2.9 MB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/004 A Note on TensorFlow 2 Syntax.mp4 2.9 MB
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  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model.mp4 2.3 MB
  • 23 - Python - Variables and Data Types/001 Python-Introduction-Course-Notes.pdf 2.1 MB
  • 30 - Python - Advanced Python Tools/002 Modules and Packages.mp4 2.1 MB
  • 24 - Python - Basic Python Syntax/003 How to Reassign Values.mp4 2.0 MB
  • 19 - Statistics - Practical Example Inferential Statistics/002 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB
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  • 24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp4 1.3 MB
  • 20 - Statistics - Hypothesis Testing/007 Online-p-value-calculator.pdf 1.2 MB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 Course-Notes-Section-6.pdf 958.9 kB
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  • 11 - Probability - Bayesian Inference/012 CDS-2017-2018-Hamilton.pdf 865.6 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 728.1 kB
  • 51 - Deep Learning - Business Case Example/001 Audiobooks-data.csv 727.8 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Audiobooks-data.csv 727.8 kB
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  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 715.1 kB
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  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Shortcuts-for-Jupyter.pdf 634.0 kB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/001 Shortcuts-for-Jupyter.pdf 634.0 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Shortcuts-for-Jupyter.pdf 634.0 kB
  • 42 - Deep Learning - Introduction to Neural Networks/001 Course-Notes-Section-2.pdf 592.0 kB
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  • 14 - Part 3 Statistics/001 Course-notes-descriptive-statistics.pdf 493.8 kB
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  • 12 - Probability - Distributions/001 Course-Notes-Probability-Distributions.pdf 475.1 kB
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  • 11 - Probability - Bayesian Inference/001 Course-Notes-Bayesian-Inference.pdf 395.3 kB
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  • 09 - Part 2 Probability/001 Course-Notes-Basic-Probability.pdf 380.0 kB
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  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/005 sklearn-Dummies-and-VIF-Exercise.ipynb 352.9 kB
  • 12 - Probability - Distributions/008 Solving-Integrals.pdf 352.1 kB
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  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 343.7 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/001 Course-Notes-Logistic-Regression.pdf 343.2 kB
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  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 336.6 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/003 365-DataScience-Diagram.pdf 330.8 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/004 365-DataScience-Diagram.pdf 330.8 kB
  • 13 - Probability - Probability in Other Fields/003 Probability-Cheat-Sheet.pdf 328.0 kB
  • 31 - Part 5 Advanced Statistical Methods in Python/001 Course-notes-regression-analysis.pdf 319.7 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 Course-notes-regression-analysis.pdf 319.7 kB
  • 01 - Part 1 Introduction/003 FAQ-The-Data-Science-Course.pdf 313.4 kB
  • 15 - Statistics - Descriptive Statistics/004 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 296.1 kB
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  • 10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics-Solutions.pdf 251.6 kB
  • 10 - Probability - Combinatorics/001 Course-Notes-Combinatorics.pdf 231.5 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/001 1.04.Real-life-example.csv 225.1 kB
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  • 37 - Advanced Statistical Methods - Cluster Analysis/001 Course-Notes-Cluster-Analysis.pdf 213.7 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/002 Course-Notes-Cluster-Analysis.pdf 213.7 kB
  • 10 - Probability - Combinatorics/006 Combinations-With-Repetition.pdf 212.4 kB
  • 13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Solutions.pdf 188.9 kB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/009 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 175.5 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 170.9 kB
  • 63 - Appendix - pandas Fundamentals/001 Sales-products.csv 155.9 kB
  • 63 - Appendix - pandas Fundamentals/012 Sales-products.csv 155.9 kB
  • 16 - Statistics - Practical Example Descriptive Statistics/001 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 150.0 kB
  • 16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 149.9 kB
  • 12 - Probability - Distributions/007 Poisson-Expected-Value-and-Variance.pdf 149.5 kB
  • 12 - Probability - Distributions/009 Normal-Distribution-Exp-and-Var.pdf 147.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/001 data-preprocessing-homework.pdf 137.7 kB
  • 16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 123.2 kB
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  • 63 - Appendix - pandas Fundamentals/001 Lending-company.csv 115.1 kB
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  • 36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Solution.ipynb 113.8 kB
  • 13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Homework.pdf 113.3 kB
  • 10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics.pdf 109.1 kB
  • 10 - Probability - Combinatorics/007 Symmetry-Explained.pdf 87.1 kB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 86.5 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.d.Solution.ipynb 86.2 kB
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  • 20 - Statistics - Hypothesis Testing/010 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 10.0 kB
  • 12 - Probability - Distributions/015 Customers-Membership.xlsx 9.9 kB
  • 20 - Statistics - Hypothesis Testing/012 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.9 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction_en.vtt 9.8 kB
  • 12 - Probability - Distributions/015 Daily-Views.xlsx 9.8 kB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/013 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.7 kB
  • 15 - Statistics - Descriptive Statistics/014 2.8.Skewness-exercise.xlsx 9.7 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions-with-comments.ipynb 9.6 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.6 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data_en.vtt 9.6 kB
  • 63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series_en.vtt 9.6 kB
  • 20 - Statistics - Hypothesis Testing/014 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.5 kB
  • 56 - Software Integration/003 Taking a Closer Look at APIs_en.vtt 9.5 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset_en.vtt 9.5 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)_en.vtt 9.5 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning_en.vtt 9.4 kB
  • 51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors_en.vtt 9.4 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5)_en.vtt 9.4 kB
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  • 18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score_en.vtt 9.3 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split-with-comments.ipynb 9.3 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature_en.vtt 9.3 kB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau_en.vtt 9.1 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence_en.vtt 9.1 kB
  • 63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames_en.vtt 9.0 kB
  • 28 - Python - Sequences/001 Lists_en.vtt 9.0 kB
  • 62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions_en.vtt 9.0 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning_en.vtt 9.0 kB
  • 13 - Probability - Probability in Other Fields/001 Probability in Finance_en.vtt 8.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.9 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.9 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/007 TensorFlow-MNIST-Part3-with-comments.ipynb 8.8 kB
  • 12 - Probability - Distributions/002 Types of Probability Distributions_en.vtt 8.8 kB
  • 51 - Deep Learning - Business Case Example/005 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.8 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained_en.vtt 8.8 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.7 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.7 kB
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  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau_en.vtt 8.7 kB
  • 40 - Part 6 Mathematics/010 Dot Product of Matrices_en.vtt 8.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.5 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch_en.vtt 8.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/016 Bank-data-testing.csv 8.5 kB
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  • 38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)_en.vtt 8.2 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.vtt 8.2 kB
  • 22 - Part 4 Introduction to Python/004 Installing Python and Jupyter_en.vtt 8.2 kB
  • 20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level_en.vtt 8.2 kB
  • 09 - Part 2 Probability/001 The Basic Probability Formula_en.vtt 8.1 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 8.1 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline_en.vtt 8.0 kB
  • 05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques_en.vtt 8.0 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression.ipynb 8.0 kB
  • 42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent_en.vtt 7.9 kB
  • 12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution_en.vtt 7.9 kB
  • 21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing_en.vtt 7.8 kB
  • 13 - Probability - Probability in Other Fields/002 Probability in Statistics_en.vtt 7.8 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/015 Testing-the-model-with-comments.ipynb 7.7 kB
  • 23 - Python - Variables and Data Types/003 Strings-Lecture-Py3.ipynb 7.7 kB
  • 56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_en.vtt 7.7 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set_en.vtt 7.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression_en.vtt 7.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters-with-comments.ipynb 7.7 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing_en.vtt 7.6 kB
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  • 20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known_en.vtt 7.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.5 kB
  • 28 - Python - Sequences/002 Using Methods_en.vtt 7.5 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.5 kB
  • 62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops_en.vtt 7.5 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split.ipynb 7.4 kB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples_en.vtt 7.4 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables_en.vtt 7.4 kB
  • 35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2)_en.vtt 7.3 kB
  • 63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]_en.vtt 7.3 kB
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  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem_en.vtt 7.2 kB
  • 62 - Appendix - Additional Python Tools/004 Triple Nested For Loops_en.vtt 7.2 kB
  • 12 - Probability - Distributions/001 Fundamentals of Probability Distributions_en.vtt 7.2 kB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2_en.vtt 7.2 kB
  • 51 - Deep Learning - Business Case Example/009 Business Case Setting an Early Stopping Mechanism_en.vtt 7.1 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python_en.vtt 7.1 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the Date Column_en.vtt 7.1 kB
  • 15 - Statistics - Descriptive Statistics/015 Variance_en.vtt 7.1 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning_en.vtt 7.0 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)_en.vtt 7.0 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider_en.vtt 7.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/012 Market-segmentation-example-Part2-with-comments.ipynb 7.0 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Minimal-example-Part-3.ipynb 7.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Exercise.ipynb 7.0 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python_en.vtt 7.0 kB
  • 42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent_en.vtt 6.9 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/012 TensorFlow-MNIST-complete.ipynb 6.9 kB
  • 60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.vtt 6.9 kB
  • 29 - Python - Iterations/004 Conditional Statements and Loops_en.vtt 6.9 kB
  • 22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks_en.vtt 6.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.vtt 6.9 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output_en.vtt 6.9 kB
  • 63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II_en.vtt 6.9 kB
  • 11 - Probability - Bayesian Inference/011 Bayes' Law_en.vtt 6.8 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared_en.vtt 6.8 kB
  • 29 - Python - Iterations/006 How to Iterate over Dictionaries_en.vtt 6.8 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases_en.vtt 6.8 kB
  • 60 - Case Study - Loading the 'absenteeism_module'/001 absenteeism-module.py 6.8 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights_en.vtt 6.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters_en.vtt 6.6 kB
  • 06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science_en.vtt 6.6 kB
  • 61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau_en.vtt 6.6 kB
  • 20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis_en.vtt 6.6 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)_en.vtt 6.6 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression_en.vtt 6.6 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/005 TensorFlow-MNIST-Part2-with-comments.ipynb 6.5 kB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram_en.vtt 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
  • 28 - Python - Sequences/004 Tuples_en.vtt 6.5 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model_en.vtt 6.5 kB
  • 09 - Part 2 Probability/004 Events and Their Complements_en.vtt 6.4 kB
  • 23 - Python - Variables and Data Types/003 Python Strings_en.vtt 6.4 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set_en.vtt 6.4 kB
  • 63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I_en.vtt 6.4 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/005 Example-bank-data.csv 6.4 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline_en.vtt 6.3 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.3 kB
  • 28 - Python - Sequences/005 Dictionaries-Solution-Py3.ipynb 6.3 kB
  • 22 - Part 4 Introduction to Python/001 Introduction to Programming_en.vtt 6.3 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.2 kB
  • 63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I_en.vtt 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
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/017 sklearn-Feature-Scaling-Exercise.ipynb 6.2 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression-with-comments.ipynb 6.2 kB
  • 22 - Part 4 Introduction to Python/002 Why Python_en.vtt 6.2 kB
  • 56 - Software Integration/005 Software Integration - Explained_en.vtt 6.2 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model_en.vtt 6.1 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)_en.vtt 6.1 kB
  • 46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training_en.vtt 6.1 kB
  • 20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples_en.vtt 6.1 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many_en.vtt 6.1 kB
  • 09 - Part 2 Probability/002 Computing Expected Values_en.vtt 6.1 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared_en.vtt 6.1 kB
  • 15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots_en.vtt 6.1 kB
  • 52 - Deep Learning - Conclusion/004 An overview of CNNs_en.vtt 6.1 kB
  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net_en.vtt 6.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/011 Market-segmentation-example-with-comments.ipynb 6.0 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)_en.vtt 6.0 kB
  • 13 - Probability - Probability in Other Fields/003 Probability in Data Science_en.vtt 6.0 kB
  • 29 - Python - Iterations/001 For Loops_en.vtt 6.0 kB
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  • 12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution_en.vtt 6.0 kB
  • 15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation_en.vtt 6.0 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept_en.vtt 6.0 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/002 Country-clusters-with-comments.ipynb 5.9 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn_en.vtt 5.9 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST Preprocess the Data - Create a Validation Set and Scale It_en.vtt 5.9 kB
  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions.ipynb 5.9 kB
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  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity_en.vtt 5.9 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_en.vtt 5.9 kB
  • 32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table_en.vtt 5.9 kB
  • 04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines_en.vtt 5.9 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created_en.vtt 5.9 kB
  • 44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0_en.vtt 5.8 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization_en.vtt 5.8 kB
  • 15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques_en.vtt 5.8 kB
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/013 sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.8 kB
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  • 01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course_en.vtt 5.8 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering_en.vtt 5.8 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/004 Categorical-data-with-comments.ipynb 5.8 kB
  • 09 - Part 2 Probability/003 Frequency_en.vtt 5.7 kB
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  • 18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error_en.vtt 5.6 kB
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  • 20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown_en.vtt 5.6 kB
  • 39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps_en.vtt 5.5 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters_en.vtt 5.5 kB
  • 15 - Statistics - Descriptive Statistics/001 Types of Data_en.vtt 5.5 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/013 Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.5 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence_en.vtt 5.5 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.vtt 5.5 kB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)_en.vtt 5.5 kB
  • 29 - Python - Iterations/002 While Loops and Incrementing_en.vtt 5.5 kB
  • 30 - Python - Advanced Python Tools/001 Object Oriented Programming_en.vtt 5.5 kB
  • 11 - Probability - Bayesian Inference/004 Union of Sets_en.vtt 5.5 kB
  • 42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks_en.vtt 5.5 kB
  • 17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution_en.vtt 5.4 kB
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  • 38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize_en.vtt 5.4 kB
  • 62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects_en.vtt 5.4 kB
  • 50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model_en.vtt 5.4 kB
  • 40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices_en.vtt 5.4 kB
  • 15 - Statistics - Descriptive Statistics/011 Mean, median and mode_en.vtt 5.3 kB
  • 56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses_en.vtt 5.3 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python_en.vtt 5.3 kB
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  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients_en.vtt 5.2 kB
  • 10 - Probability - Combinatorics/006 Solving Combinations_en.vtt 5.2 kB
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  • 18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score_en.vtt 5.1 kB
  • 40 - Part 6 Mathematics/008 Transpose of a Matrix_en.vtt 5.1 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression_en.vtt 5.1 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets_en.vtt 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
  • 34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression.ipynb 5.0 kB
  • 63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()_en.vtt 5.0 kB
  • 14 - Part 3 Statistics/001 Population and Sample_en.vtt 5.0 kB
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  • 63 - Appendix - pandas Fundamentals/006 Using .sort_values()_en.vtt 5.0 kB
  • 57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise_en.vtt 5.0 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module_en.vtt 5.0 kB
  • 56 - Software Integration/004 Communication between Software Products through Text Files_en.vtt 5.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables_en.vtt 4.9 kB
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  • 42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs_en.vtt 4.9 kB
  • 12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution_en.vtt 4.9 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/008 Understanding-Logistic-Regression-Tables-Solution.ipynb 4.9 kB
  • 20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)_en.vtt 4.9 kB
  • 08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions_en.vtt 4.9 kB
  • 20 - Statistics - Hypothesis Testing/007 p-value_en.vtt 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
  • 12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution_en.vtt 4.8 kB
  • 11 - Probability - Bayesian Inference/001 Sets and Events_en.vtt 4.8 kB
  • 42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning_en.vtt 4.8 kB
  • 52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches_en.vtt 4.8 kB
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  • 52 - Deep Learning - Conclusion/001 Summary on What You've Learned_en.vtt 4.8 kB
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  • 45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions_en.vtt 4.8 kB
  • 54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model_en.vtt 4.8 kB
  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework_en.vtt 4.8 kB
  • 20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error_en.vtt 4.7 kB
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  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python_en.vtt 4.7 kB
  • 40 - Part 6 Mathematics/004 Scalars-Vectors-and-Matrices.ipynb 4.7 kB
  • 38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters.ipynb 4.6 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic_en.vtt 4.6 kB
  • 01 - Part 1 Introduction/002 What Does the Course Cover_en.vtt 4.6 kB
  • 27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.6 kB
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  • 36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean_en.vtt 4.5 kB
  • 59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)_en.vtt 4.5 kB
  • 02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics_en.vtt 4.5 kB
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  • 12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution_en.vtt 4.4 kB
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  • 46 - Deep Learning - Overfitting/003 What is Validation_en.vtt 4.4 kB
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  • 33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation_en.vtt 4.3 kB
  • 18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)_en.vtt 4.3 kB
  • 37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis_en.vtt 4.3 kB
  • 51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data_en.vtt 4.3 kB
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  • 49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding_en.vtt 4.3 kB
  • 15 - Statistics - Descriptive Statistics/021 Correlation Coefficient_en.vtt 4.3 kB
  • 53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent_en.vtt 4.3 kB
  • 15 - Statistics - Descriptive Statistics/002 Levels of Measurement_en.vtt 4.3 kB
  • 10 - Probability - Combinatorics/005 Solving Variations without Repetition_en.vtt 4.3 kB
  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent_en.vtt 4.2 kB
  • 11 - Probability - Bayesian Inference/010 The Multiplication Law_en.vtt 4.2 kB
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  • 55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset_en.vtt 4.2 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression-tables-fixed-error.ipynb 4.2 kB
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  • 42 - Deep Learning - Introduction to Neural Networks/002 Training the Model_en.vtt 4.0 kB
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  • 40 - Part 6 Mathematics/001 What is a Matrix_en.vtt 4.0 kB
  • 58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column_en.vtt 4.0 kB
  • 36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model_en.vtt 4.0 kB
  • 43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)_en.vtt 3.9 kB
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  • 37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites_en.vtt 3.7 kB
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  • 10 - Probability - Combinatorics/010 A Recap of Combinatorics_en.vtt 3.3 kB
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  • 48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)_en.vtt 3.1 kB
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  • 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

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