24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.36MB
25. Python - Other Python Operators/1. Comparison Operators.mp410.17MB
25. Python - Other Python Operators/3. Logical and Identity Operators.mp430.05MB
26. Python - Conditional Statements/1. The IF Statement.mp423.24MB
26. Python - Conditional Statements/3. The ELSE Statement.mp423.29MB
26. Python - Conditional Statements/4. The ELIF Statement.mp453.34MB
26. Python - Conditional Statements/5. A Note on Boolean Values.mp420MB
27. Python - Python Functions/1. Defining a Function in Python.mp414.76MB
27. Python - Python Functions/2. How to Create a Function with a Parameter.mp438.11MB
27. Python - Python Functions/3. Defining a Function in Python - Part II.mp425.25MB
27. Python - Python Functions/4. How to Use a Function within a Function.mp48.14MB
27. Python - Python Functions/5. Conditional Statements and Functions.mp415.69MB
27. Python - Python Functions/6. Functions Containing a Few Arguments.mp414.71MB
27. Python - Python Functions/7. Built-in Functions in Python.mp422.02MB
28. Python - Sequences/1. Lists.mp437.8MB
28. Python - Sequences/3. Using Methods.mp437.6MB
28. Python - Sequences/5. List Slicing.mp430.77MB
28. Python - Sequences/6. Tuples.mp429.5MB
28. Python - Sequences/7. Dictionaries.mp441.69MB
29. Python - Iterations/1. For Loops.mp423.6MB
29. Python - Iterations/3. While Loops and Incrementing.mp428.43MB
29. Python - Iterations/4. Lists with the range() Function.mp425.79MB
29. Python - Iterations/6. Conditional Statements and Loops.mp427.77MB
29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB
29. Python - Iterations/8. How to Iterate over Dictionaries.mp429.65MB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.87MB
30. Python - Advanced Python Tools/3. Modules and Packages.mp48.5MB
30. Python - Advanced Python Tools/5. What is the Standard Library.mp418.03MB
30. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.94MB
31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.32MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/1. The Linear Regression Model.mp457.38MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/10. Using Seaborn for Graphs.mp412.25MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/11. How to Interpret the Regression Table.mp444.64MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/13. Decomposition of Variability.mp449.67MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/15. What is the OLS.mp428.32MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/17. R-Squared.mp441.04MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/3. Correlation vs Regression.mp414.74MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp45.13MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/7. Python Packages Installation.mp440.59MB
32. Advanced Statistical Methods - Linear Regression with StatsModels/8. First Regression in Python.mp444.57MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp421.53MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp435.68MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp442.71MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp431.51MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp428.71MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp455.67MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp424.69MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp454.84MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp416.42MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp421.86MB
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp412.61MB
34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp427.25MB
34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp429.51MB
34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.mp412.31MB
34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp439.09MB
34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp434.89MB
34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp425.97MB
34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp416.96MB
34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp449.18MB
34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are we Going to Approach this Section.mp419.41MB
34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp434.78MB
34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432.01MB
34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp420.08MB
34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp430.88MB
35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp497.09MB
35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp446.01MB
35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp423.7MB
35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp456.05MB
35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp457.89MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.69MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.02MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.43MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp429.09MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.11MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.35MB
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.36MB
49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.79MB
49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.85MB
49. Deep Learning - Preprocessing/3. Standardization.mp450.99MB
49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.6MB
49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.94MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.31MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4111.66MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.79MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.33MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.15MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.82MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.94MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.51MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.04MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.95MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB
50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST The Dataset.mp413.38MB
50. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp440.96MB
50. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp429.52MB
50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp418.66MB
50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Importing the Relevant Packages and Loading the Data.mp416.32MB
50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp429.04MB
50. Deep Learning - Classifying on the MNIST Dataset/6. MNIST Preprocess the Data - Shuffle and Batch.mp441.52MB
50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Outline the Model.mp428.23MB
50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Select the Loss and the Optimizer.mp413.91MB
51. Deep Learning - Business Case Example/1. Business Case Exploring the Dataset and Identifying Predictors.mp466.28MB
51. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp410.8MB
51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp47.31MB
51. Deep Learning - Business Case Example/3. Business Case Balancing the Dataset.mp430.44MB
51. Deep Learning - Business Case Example/4. Business Case Preprocessing the Data.mp484.34MB
51. Deep Learning - Business Case Example/6. Business Case Load the Preprocessed Data.mp417.57MB
51. Deep Learning - Business Case Example/8. Business Case Learning and Interpreting the Result.mp431.19MB
51. Deep Learning - Business Case Example/9. Business Case Setting an Early Stopping Mechanism.mp449.82MB
52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp439.76MB
52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.13MB
52. Deep Learning - Conclusion/4. An overview of CNNs.mp458.8MB
52. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.26MB
52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.78MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/2. How to Install TensorFlow 1.mp411.36MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/4. TensorFlow Intro.mp447.7MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/5. Actual Introduction to TensorFlow.mp417.41MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/6. Types of File Formats, supporting Tensors.mp420.34MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.5MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/8. Basic NN Example with TF Loss Function and Gradient Descent.mp432.52MB
53. Appendix Deep Learning - TensorFlow 1 Introduction/9. Basic NN Example with TF Model Output.mp437.39MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.83MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.59MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.9MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.39MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.86MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.91MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.85MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/8. MNIST Learning.mp446.69MB
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.78MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/1. Business Case Getting Acquainted with the Dataset.mp487.66MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp411.21MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp436.38MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/2. Business Case Outlining the Solution.mp412.22MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/3. The Importance of Working with a Balanced Dataset.mp439.42MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/4. Business Case Preprocessing.mp4103.42MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/6. Creating a Data Provider.mp476.35MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/7. Business Case Model Outline.mp453.13MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/8. Business Case Optimization.mp441.53MB
55. Appendix Deep Learning - TensorFlow 1 Business Case/9. Business Case Interpretation.mp425.75MB
56. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp469.04MB
56. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4104.08MB
56. Software Integration/5. Taking a Closer Look at APIs.mp4115.6MB
56. Software Integration/7. Communication between Software Products through Text Files.mp460.34MB
57. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp452.31MB
57. Case Study - What's Next in the Course/2. The Business Task.mp439.16MB
57. Case Study - What's Next in the Course/3. Introducing the Data Set.mp440.87MB
58. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp440.58MB
58. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp481.11MB
58. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp413.74MB
58. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp474.61MB
58. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp438.73MB
58. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp423.15MB
58. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp414.01MB
58. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp425.67MB
58. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp457.28MB
58. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp447.79MB
58. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp427.96MB
58. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp461.9MB
58. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp429.52MB
58. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp439.6MB
58. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp421.63MB
58. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp427.86MB
58. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp420.19MB
58. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp461.77MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp427.55MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp440.41MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp439.56MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp449.07MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp437.46MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp444.49MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp445.8MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp416.76MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp420.6MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp452.77MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp441.62MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp438.88MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp452.38MB
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.52MB
60. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp425.49MB
60. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp454.25MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp456.56MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp459.34MB
61. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.38MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.86MB
9. Part 2 Probability/1. The Basic Probability Formula.mp485.92MB
9. Part 2 Probability/3. Computing Expected Values.mp475.68MB
9. Part 2 Probability/5. Frequency.mp461.74MB
9. Part 2 Probability/7. Events and Their Complements.mp459.16MB