Name                                                                                   Size  
 Parent Directory                                                                         -   
 1.1 Course Resources.html                                                              122   
 31. Python Conditional Statement Coding Exercise.html                                  156   
 33. Download the Complete Notebook Here.html                                           242   
 13. A Note for the Next Lesson.html                                                    476   
 34. Any Feedback on this Section.html                                                  512   
 33.2 04 Valuation Tool.ipynb.zip                                                       2.9K  
 33.3 boston_valuation.py                                                               3.1K  
 19. Introduction to Model Evaluation.srt                                               3.8K  
 18. How to Calculate the Model Fit with R-Squared.srt                                  4.4K  
 1. Defining the Problem.srt                                                            6.5K  
 15. Understanding Multivariable Regression.srt                                         7.5K  
 9. Introduction to Correlation Understanding Strength & Direction.srt                  8.4K  
 2. Gathering the Boston House Price Data.srt                                           8.7K  
 6. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt             9.0K  
 17. Running a Multivariable Regression.srt                                             9.8K  
 21. How to Interpret Coefficients using p-Values and Statistical Significance.srt       11K  
 16. How to Shuffle and Split Training & Testing Data.srt                                12K  
 8. Understanding Descriptive Statistics the Mean vs the Median.srt                      12K  
 5. Visualising Data (Part 1) Historams, Distributions & Outliers.srt                    14K  
 24. How to Analyse and Plot Regression Residuals.srt                                    15K  
 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt  15K  
 3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt         16K  
 10. Calculating Correlations and the Problem posed by Multicollinearity.srt             18K  
 25. Residual Analysis (Part 1) Predicted vs Actual Values.srt                           18K  
 4. Clean and Explore the Data (Part 2) Find Missing Values.srt                          19K  
 12. Techniques to Style Scatter Plots.srt                                               21K  
 7. Working with Index Data, Pandas Series, and Dummy Variables.srt                      21K  
 29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt     21K  
 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt             21K  
 20. Improving the Model by Transforming the Data.srt                                    22K  
 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt          23K  
 23. Model Simplification & Baysian Information Criterion.srt                            23K  
 27. Making Predictions (Part 1) MSE & R-Squared.srt                                     24K  
 11. Visualising Correlations with a Heatmap.srt                                         24K  
 22. Understanding VIF & Testing for Multicollinearity.srt                               26K  
 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt    28K  
 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt           29K  
 33.1 04 Multivariable Regression.ipynb.zip                                             3.5M  
 19. Introduction to Model Evaluation.mp4                                                16M  
 18. How to Calculate the Model Fit with R-Squared.mp4                                   32M  
 9. Introduction to Correlation Understanding Strength & Direction.mp4                   33M  
 1. Defining the Problem.mp4                                                             40M  
 15. Understanding Multivariable Regression.mp4                                          49M  
 17. Running a Multivariable Regression.mp4                                              56M  
 2. Gathering the Boston House Price Data.mp4                                            56M  
 6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4              57M  
 8. Understanding Descriptive Statistics the Mean vs the Median.mp4                      62M  
 24. How to Analyse and Plot Regression Residuals.mp4                                    64M  
 16. How to Shuffle and Split Training & Testing Data.mp4                                64M  
 5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4                    65M  
 21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4       65M  
 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4  85M  
 3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4         87M  
 10. Calculating Correlations and the Problem posed by Multicollinearity.mp4            111M  
 25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4                          124M  
 20. Improving the Model by Transforming the Data.mp4                                   127M  
 12. Techniques to Style Scatter Plots.mp4                                              129M  
 29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4    131M  
 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4            134M  
 4. Clean and Explore the Data (Part 2) Find Missing Values.mp4                         135M  
 7. Working with Index Data, Pandas Series, and Dummy Variables.mp4                     141M  
 22. Understanding VIF & Testing for Multicollinearity.mp4                              144M  
 23. Model Simplification & Baysian Information Criterion.mp4                           150M  
 27. Making Predictions (Part 1) MSE & R-Squared.mp4                                    153M  
 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4         153M  
 11. Visualising Correlations with a Heatmap.mp4                                        169M  
 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4          214M  
 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4   244M