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

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