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32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 2024-07-11 10:21 244M
14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 2024-07-11 10:20 214M
11. Visualising Correlations with a Heatmap.mp4 2024-07-11 10:20 169M
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 2024-07-11 10:20 153M
27. Making Predictions (Part 1) MSE & R-Squared.mp4 2024-07-11 10:20 153M
23. Model Simplification & Baysian Information Criterion.mp4 2024-07-11 10:20 150M
22. Understanding VIF & Testing for Multicollinearity.mp4 2024-07-11 10:20 144M
7. Working with Index Data, Pandas Series, and Dummy Variables.mp4 2024-07-11 10:21 141M
4. Clean and Explore the Data (Part 2) Find Missing Values.mp4 2024-07-11 10:21 135M
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 2024-07-11 10:21 134M
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 2024-07-11 10:20 131M
12. Techniques to Style Scatter Plots.mp4 2024-07-11 10:20 129M
20. Improving the Model by Transforming the Data.mp4 2024-07-11 10:20 127M
25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 2024-07-11 10:20 124M
10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 2024-07-11 10:20 111M
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 2024-07-11 10:20 87M
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 2024-07-11 10:20 85M
21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 2024-07-11 10:20 65M
5. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 2024-07-11 10:21 65M
16. How to Shuffle and Split Training & Testing Data.mp4 2024-07-11 10:20 64M
24. How to Analyse and Plot Regression Residuals.mp4 2024-07-11 10:20 64M
8. Understanding Descriptive Statistics the Mean vs the Median.mp4 2024-07-11 10:21 62M
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 2024-07-11 10:21 57M
2. Gathering the Boston House Price Data.mp4 2024-07-11 10:20 56M
17. Running a Multivariable Regression.mp4 2024-07-11 10:20 56M
15. Understanding Multivariable Regression.mp4 2024-07-11 10:20 49M
1. Defining the Problem.mp4 2024-07-11 10:20 40M
9. Introduction to Correlation Understanding Strength & Direction.mp4 2024-07-11 10:21 33M
18. How to Calculate the Model Fit with R-Squared.mp4 2024-07-11 10:20 32M
19. Introduction to Model Evaluation.mp4 2024-07-11 10:20 16M
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33.1 04 Multivariable Regression.ipynb.zip 2024-07-11 10:21 3.5M
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14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt 2024-07-11 10:20 29K
32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt 2024-07-11 10:21 28K
22. Understanding VIF & Testing for Multicollinearity.srt 2024-07-11 10:20 26K
11. Visualising Correlations with a Heatmap.srt 2024-07-11 10:20 24K
27. Making Predictions (Part 1) MSE & R-Squared.srt 2024-07-11 10:20 24K
23. Model Simplification & Baysian Information Criterion.srt 2024-07-11 10:20 23K
26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt 2024-07-11 10:20 23K
20. Improving the Model by Transforming the Data.srt 2024-07-11 10:20 22K
30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt 2024-07-11 10:21 21K
29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt 2024-07-11 10:20 21K
7. Working with Index Data, Pandas Series, and Dummy Variables.srt 2024-07-11 10:21 21K
12. Techniques to Style Scatter Plots.srt 2024-07-11 10:20 21K
4. Clean and Explore the Data (Part 2) Find Missing Values.srt 2024-07-11 10:21 19K
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25. Residual Analysis (Part 1) Predicted vs Actual Values.srt 2024-07-11 10:20 18K
10. Calculating Correlations and the Problem posed by Multicollinearity.srt 2024-07-11 10:20 18K
3. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt 2024-07-11 10:20 16K
28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt 2024-07-11 10:20 15K
24. How to Analyse and Plot Regression Residuals.srt 2024-07-11 10:20 15K
5. Visualising Data (Part 1) Historams, Distributions & Outliers.srt 2024-07-11 10:21 14K
8. Understanding Descriptive Statistics the Mean vs the Median.srt 2024-07-11 10:21 12K
16. How to Shuffle and Split Training & Testing Data.srt 2024-07-11 10:20 12K
21. How to Interpret Coefficients using p-Values and Statistical Significance.srt 2024-07-11 10:20 11K
17. Running a Multivariable Regression.srt 2024-07-11 10:20 9.8K
6. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt 2024-07-11 10:21 9.0K
2. Gathering the Boston House Price Data.srt 2024-07-11 10:20 8.7K
9. Introduction to Correlation Understanding Strength & Direction.srt 2024-07-11 10:21 8.4K
15. Understanding Multivariable Regression.srt 2024-07-11 10:20 7.5K
1. Defining the Problem.srt 2024-07-11 10:20 6.5K
18. How to Calculate the Model Fit with R-Squared.srt 2024-07-11 10:20 4.4K
19. Introduction to Model Evaluation.srt 2024-07-11 10:20 3.8K
33.3 boston_valuation.py 2024-07-11 10:21 3.1K
33.2 04 Valuation Tool.ipynb.zip 2024-07-11 10:21 2.9K
34. Any Feedback on this Section.html 2024-07-11 10:21 512
13. A Note for the Next Lesson.html 2024-07-11 10:20 476
33. Download the Complete Notebook Here.html 2024-07-11 10:21 242
31. Python Conditional Statement Coding Exercise.html 2024-07-11 10:21 156
1.1 Course Resources.html 2024-07-11 10:20 122