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文档列表

13. Assembling a feature engineering pipeline/2. Regression pipeline.mp4 165.2 MB
13. Assembling a feature engineering pipeline/1. Classification pipeline.mp4 142.6 MB
4. Missing Data Imputation/8. Random sample imputation.mp4 107.6 MB
6. Categorical Variable Encoding/3. One-hot-encoding Demo.mp4 95.8 MB
4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.mp4 92.4 MB
4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.mp4 84.3 MB
8. Discretisation/11. Discretisation with decision trees using Scikit-learn.mp4 84.1 MB
8. Discretisation/3. Equal-width discretisation Demo.mp4 82.9 MB
6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.mp4 82.3 MB
6. Categorical Variable Encoding/19. Rare label encoding Demo.mp4 72.8 MB
3. Variable Characteristics/5. Linear models assumptions.mp4 72.2 MB
6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.mp4 72.1 MB
6. Categorical Variable Encoding/7. Ordinal encoding Demo.mp4 60.3 MB
6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.mp4 60.0 MB
12. Engineering datetime variables/2. Engineering dates Demo.mp4 56.6 MB
4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.mp4 54.7 MB
4. Missing Data Imputation/3. Mean or median imputation.mp4 54.7 MB
9. Outlier Handling/2. Outlier trimming.mp4 53.6 MB
4. Missing Data Imputation/6. Frequent category imputation.mp4 52.2 MB
7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.mp4 51.8 MB
3. Variable Characteristics/7. Outliers.mp4 50.7 MB
8. Discretisation/5. Equal-frequency discretisation Demo.mp4 49.6 MB
7. Variable Transformation/3. variable Transformation with Scikit-learn.mp4 49.4 MB
4. Missing Data Imputation/2. Complete Case Analysis.mp4 48.9 MB
10. Feature Scaling/13. Scaling to vector unit length Demo.mp4 48.6 MB
6. Categorical Variable Encoding/14. Probability ratio encoding.mp4 47.9 MB
11. Engineering mixed variables/2. Engineering mixed variables Demo.mp4 47.7 MB
6. Categorical Variable Encoding/16. Weight of Evidence Demo.mp4 47.3 MB
10. Feature Scaling/5. Mean normalisation Demo.mp4 47.3 MB
9. Outlier Handling/3. Outlier capping with IQR.mp4 45.7 MB
6. Categorical Variable Encoding/13. Mean encoding Demo.mp4 44.1 MB
9. Outlier Handling/1. Outlier Engineering Intro.mp4 44.0 MB
10. Feature Scaling/3. Standardisation Demo.mp4 43.6 MB
4. Missing Data Imputation/16. Introduction to Feature-engine.mp4 42.4 MB
3. Variable Characteristics/2. Missing data.mp4 42.1 MB
4. Missing Data Imputation/4. Arbitrary value imputation.mp4 42.0 MB
4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.mp4 40.8 MB
4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.mp4 40.5 MB
8. Discretisation/9. Discretisation plus encoding Demo.mp4 38.0 MB
4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.mp4 37.4 MB
9. Outlier Handling/4. Outlier capping with mean and std.mp4 36.3 MB
4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.mp4 35.8 MB
6. Categorical Variable Encoding/1. Categorical encoding Introduction.mp4 35.7 MB
3. Variable Characteristics/4. Rare Labels - categorical variables.mp4 35.5 MB
12. Engineering datetime variables/3. Engineering time variables and different timezones.mp4 35.1 MB
1. Introduction/2. Course curriculum overview.mp4 35.0 MB
1. Introduction/1. Introduction.mp4 34.5 MB
3. Variable Characteristics/6. Variable distribution.mp4 34.4 MB
6. Categorical Variable Encoding/9. Count encoding Demo.mp4 34.1 MB
10. Feature Scaling/12. Scaling to vector unit length.mp4 33.5 MB
6. Categorical Variable Encoding/2. One hot encoding.mp4 33.3 MB
10. Feature Scaling/9. MaxAbsScaling Demo.mp4 33.0 MB
4. Missing Data Imputation/9. Adding a missing indicator.mp4 32.6 MB
3. Variable Characteristics/3. Cardinality - categorical variables.mp4 32.5 MB
6. Categorical Variable Encoding/20. Binary encoding and feature hashing.mp4 32.4 MB
1. Introduction/8.1 FeatureEngineeringSlides.zip.zip 31.0 MB
4. Missing Data Imputation/1. Introduction to missing data imputation.mp4 30.8 MB
8. Discretisation/12. Discretisation with decision trees using Feature-engine.mp4 29.8 MB
4. Missing Data Imputation/7. Missing category imputation.mp4 29.5 MB
4. Missing Data Imputation/5. End of distribution imputation.mp4 29.5 MB
2. Variable Types/2. Numerical variables.mp4 28.2 MB
4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.mp4 28.0 MB
8. Discretisation/10. Discretisation with classification trees.mp4 27.9 MB
10. Feature Scaling/2. Standardisation.mp4 27.8 MB
4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.mp4 27.2 MB
10. Feature Scaling/7. MinMaxScaling Demo.mp4 27.1 MB
8. Discretisation/13. Domain knowledge discretisation.mp4 26.9 MB
4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.mp4 25.8 MB
9. Outlier Handling/5. Outlier capping with quantiles.mp4 25.6 MB
7. Variable Transformation/4. Variable transformation with Feature-engine.mp4 24.8 MB
6. Categorical Variable Encoding/18. Rare label encoding.mp4 24.4 MB
12. Engineering datetime variables/1. Engineering datetime variables.mp4 24.3 MB
8. Discretisation/4. Equal-frequency discretisation.mp4 23.6 MB
8. Discretisation/2. Equal-width discretisation.mp4 22.6 MB
3. Variable Characteristics/1. Variable characteristics.mp4 21.9 MB
10. Feature Scaling/1. Feature scaling Introduction.mp4 21.6 MB
6. Categorical Variable Encoding/15. Weight of evidence (WoE).mp4 21.6 MB
4. Missing Data Imputation/21. Missing category imputation with Feature-engine.mp4 21.4 MB
3. Variable Characteristics/8. Variable magnitude.mp4 20.9 MB
10. Feature Scaling/4. Mean normalisation.mp4 20.8 MB
9. Outlier Handling/6. Arbitrary capping.mp4 20.6 MB
8. Discretisation/6. K-means discretisation.mp4 19.8 MB
8. Discretisation/7. K-means discretisation Demo.mp4 19.7 MB
7. Variable Transformation/1. Variable Transformation Introduction.mp4 19.6 MB
2. Variable Types/3. Categorical variables.mp4 19.3 MB
6. Categorical Variable Encoding/4. One hot encoding of top categories.mp4 19.0 MB
10. Feature Scaling/6. Scaling to minimum and maximum values.mp4 17.9 MB
10. Feature Scaling/11. Robust Scaling Demo.mp4 17.4 MB
4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.mp4 16.9 MB
4. Missing Data Imputation/22. Random sample imputation with Feature-engine.mp4 16.9 MB
6. Categorical Variable Encoding/8. Count or frequency encoding.mp4 16.5 MB
8. Discretisation/1. Discretisation Introduction.mp4 16.2 MB
2. Variable Types/1. Variables Intro.mp4 16.0 MB
11. Engineering mixed variables/1. Engineering mixed variables.mp4 16.0 MB
10. Feature Scaling/8. Maximum absolute scaling.mp4 15.3 MB
8. Discretisation/8. Discretisation plus categorical encoding.mp4 14.0 MB
10. Feature Scaling/10. Scaling to median and quantiles.mp4 13.6 MB
6. Categorical Variable Encoding/10. Target guided ordinal encoding.mp4 13.5 MB
6. Categorical Variable Encoding/12. Mean encoding.mp4 13.5 MB
2. Variable Types/5. Mixed variables.mp4 11.8 MB
1. Introduction/3. Course requirements.mp4 11.2 MB
2. Variable Types/5.1 sample_s2.csv.csv 10.4 MB
2. Variable Types/4. Date and time variables.mp4 10.3 MB
6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.mp4 9.9 MB
1. Introduction/6.1 HandsOnPythonCode.zip.zip 9.7 MB
3. Variable Characteristics/9.1 ML_Comparison.pdf.pdf 304.8 kB
4. Missing Data Imputation/24.1 NA_methods_Comparison.pdf.pdf 280.4 kB
4. Missing Data Imputation/8. Random sample imputation.srt 18.1 kB
6. Categorical Variable Encoding/3. One-hot-encoding Demo.srt 18.0 kB
13. Assembling a feature engineering pipeline/2. Regression pipeline.srt 17.2 kB
4. Missing Data Imputation/8. Random sample imputation.vtt 16.0 kB
13. Assembling a feature engineering pipeline/1. Classification pipeline.srt 15.9 kB
6. Categorical Variable Encoding/3. One-hot-encoding Demo.vtt 15.8 kB
13. Assembling a feature engineering pipeline/2. Regression pipeline.vtt 15.2 kB
13. Assembling a feature engineering pipeline/1. Classification pipeline.vtt 14.2 kB
8. Discretisation/11. Discretisation with decision trees using Scikit-learn.srt 13.4 kB
4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.srt 13.2 kB
8. Discretisation/3. Equal-width discretisation Demo.srt 12.8 kB
6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.srt 12.7 kB
6. Categorical Variable Encoding/19. Rare label encoding Demo.srt 12.3 kB
8. Discretisation/11. Discretisation with decision trees using Scikit-learn.vtt 11.9 kB
3. Variable Characteristics/5. Linear models assumptions.srt 11.8 kB
4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.vtt 11.6 kB
8. Discretisation/3. Equal-width discretisation Demo.vtt 11.3 kB
6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.vtt 11.2 kB
6. Categorical Variable Encoding/19. Rare label encoding Demo.vtt 10.9 kB
3. Variable Characteristics/7. Outliers.srt 10.7 kB
4. Missing Data Imputation/3. Mean or median imputation.srt 10.5 kB
3. Variable Characteristics/5. Linear models assumptions.vtt 10.5 kB
6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.srt 9.9 kB
6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.srt 9.6 kB
6. Categorical Variable Encoding/7. Ordinal encoding Demo.srt 9.6 kB
3. Variable Characteristics/7. Outliers.vtt 9.5 kB
4. Missing Data Imputation/3. Mean or median imputation.vtt 9.3 kB
12. Engineering datetime variables/2. Engineering dates Demo.srt 9.3 kB
4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.srt 9.2 kB
3. Variable Characteristics/2. Missing data.srt 9.2 kB
6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.vtt 8.8 kB
4. Missing Data Imputation/2. Complete Case Analysis.srt 8.7 kB
7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.srt 8.7 kB
4. Missing Data Imputation/4. Arbitrary value imputation.srt 8.6 kB
6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.vtt 8.6 kB
6. Categorical Variable Encoding/7. Ordinal encoding Demo.vtt 8.5 kB
9. Outlier Handling/2. Outlier trimming.srt 8.5 kB
4. Missing Data Imputation/6. Frequent category imputation.srt 8.4 kB
12. Engineering datetime variables/2. Engineering dates Demo.vtt 8.2 kB
4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.vtt 8.2 kB
6. Categorical Variable Encoding/16. Weight of Evidence Demo.srt 8.2 kB
6. Categorical Variable Encoding/1. Categorical encoding Introduction.srt 8.1 kB
3. Variable Characteristics/2. Missing data.vtt 8.1 kB
9. Outlier Handling/1. Outlier Engineering Intro.srt 7.9 kB
8. Discretisation/5. Equal-frequency discretisation Demo.srt 7.9 kB
7. Variable Transformation/3. variable Transformation with Scikit-learn.srt 7.8 kB
7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.vtt 7.7 kB
4. Missing Data Imputation/2. Complete Case Analysis.vtt 7.7 kB
6. Categorical Variable Encoding/20. Binary encoding and feature hashing.srt 7.7 kB
4. Missing Data Imputation/4. Arbitrary value imputation.vtt 7.7 kB
9. Outlier Handling/2. Outlier trimming.vtt 7.6 kB
4. Missing Data Imputation/6. Frequent category imputation.vtt 7.5 kB
11. Engineering mixed variables/2. Engineering mixed variables Demo.srt 7.4 kB
1. Introduction/2. Course curriculum overview.srt 7.4 kB
6. Categorical Variable Encoding/14. Probability ratio encoding.srt 7.3 kB
6. Categorical Variable Encoding/16. Weight of Evidence Demo.vtt 7.3 kB
6. Categorical Variable Encoding/1. Categorical encoding Introduction.vtt 7.3 kB
6. Categorical Variable Encoding/2. One hot encoding.srt 7.1 kB
9. Outlier Handling/1. Outlier Engineering Intro.vtt 7.0 kB
8. Discretisation/5. Equal-frequency discretisation Demo.vtt 7.0 kB
1. Introduction/1. Introduction.srt 7.0 kB
7. Variable Transformation/3. variable Transformation with Scikit-learn.vtt 7.0 kB
9. Outlier Handling/3. Outlier capping with IQR.srt 6.9 kB
2. Variable Types/2. Numerical variables.srt 6.9 kB
6. Categorical Variable Encoding/20. Binary encoding and feature hashing.vtt 6.8 kB
10. Feature Scaling/12. Scaling to vector unit length.srt 6.8 kB
10. Feature Scaling/2. Standardisation.srt 6.7 kB
8. Discretisation/9. Discretisation plus encoding Demo.srt 6.7 kB
4. Missing Data Imputation/9. Adding a missing indicator.srt 6.7 kB
4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.srt 6.7 kB
3. Variable Characteristics/6. Variable distribution.srt 6.6 kB
1. Introduction/2. Course curriculum overview.vtt 6.6 kB
11. Engineering mixed variables/2. Engineering mixed variables Demo.vtt 6.6 kB
4. Missing Data Imputation/16. Introduction to Feature-engine.srt 6.6 kB
6. Categorical Variable Encoding/13. Mean encoding Demo.srt 6.5 kB
3. Variable Characteristics/3. Cardinality - categorical variables.srt 6.5 kB
6. Categorical Variable Encoding/14. Probability ratio encoding.vtt 6.5 kB
6. Categorical Variable Encoding/2. One hot encoding.vtt 6.3 kB
10. Feature Scaling/5. Mean normalisation Demo.srt 6.3 kB
3. Variable Characteristics/4. Rare Labels - categorical variables.srt 6.2 kB
1. Introduction/1. Introduction.vtt 6.2 kB
9. Outlier Handling/3. Outlier capping with IQR.vtt 6.2 kB
4. Missing Data Imputation/5. End of distribution imputation.srt 6.2 kB
10. Feature Scaling/13. Scaling to vector unit length Demo.srt 6.2 kB
2. Variable Types/2. Numerical variables.vtt 6.1 kB
10. Feature Scaling/12. Scaling to vector unit length.vtt 6.0 kB
10. Feature Scaling/2. Standardisation.vtt 6.0 kB
4. Missing Data Imputation/9. Adding a missing indicator.vtt 6.0 kB
8. Discretisation/9. Discretisation plus encoding Demo.vtt 5.9 kB
4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.vtt 5.9 kB
3. Variable Characteristics/6. Variable distribution.vtt 5.9 kB
4. Missing Data Imputation/16. Introduction to Feature-engine.vtt 5.8 kB
6. Categorical Variable Encoding/13. Mean encoding Demo.vtt 5.8 kB
3. Variable Characteristics/3. Cardinality - categorical variables.vtt 5.8 kB
10. Feature Scaling/5. Mean normalisation Demo.vtt 5.6 kB
10. Feature Scaling/3. Standardisation Demo.srt 5.6 kB
12. Engineering datetime variables/1. Engineering datetime variables.srt 5.6 kB
7. Variable Transformation/1. Variable Transformation Introduction.srt 5.6 kB
8. Discretisation/10. Discretisation with classification trees.srt 5.6 kB
3. Variable Characteristics/4. Rare Labels - categorical variables.vtt 5.5 kB
12. Engineering datetime variables/3. Engineering time variables and different timezones.srt 5.5 kB
4. Missing Data Imputation/5. End of distribution imputation.vtt 5.5 kB
10. Feature Scaling/13. Scaling to vector unit length Demo.vtt 5.5 kB
4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.srt 5.4 kB
4. Missing Data Imputation/1. Introduction to missing data imputation.srt 5.4 kB
6. Categorical Variable Encoding/18. Rare label encoding.srt 5.3 kB
4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.srt 5.2 kB
6. Categorical Variable Encoding/15. Weight of evidence (WoE).srt 5.2 kB
6. Categorical Variable Encoding/9. Count encoding Demo.srt 5.0 kB
7. Variable Transformation/1. Variable Transformation Introduction.vtt 5.0 kB
10. Feature Scaling/3. Standardisation Demo.vtt 5.0 kB
10. Feature Scaling/4. Mean normalisation.srt 5.0 kB
8. Discretisation/10. Discretisation with classification trees.vtt 5.0 kB
12. Engineering datetime variables/1. Engineering datetime variables.vtt 5.0 kB
4. Missing Data Imputation/7. Missing category imputation.srt 4.9 kB
9. Outlier Handling/4. Outlier capping with mean and std.srt 4.9 kB
12. Engineering datetime variables/3. Engineering time variables and different timezones.vtt 4.8 kB
8. Discretisation/6. K-means discretisation.srt 4.8 kB
4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.srt 4.8 kB
4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.vtt 4.8 kB
3. Variable Characteristics/10. Bonus Additional reading resources.html 4.8 kB
8. Discretisation/4. Equal-frequency discretisation.srt 4.8 kB
4. Missing Data Imputation/1. Introduction to missing data imputation.vtt 4.8 kB
6. Categorical Variable Encoding/18. Rare label encoding.vtt 4.7 kB
2. Variable Types/3. Categorical variables.srt 4.7 kB
10. Feature Scaling/9. MaxAbsScaling Demo.srt 4.7 kB
10. Feature Scaling/1. Feature scaling Introduction.srt 4.7 kB
6. Categorical Variable Encoding/15. Weight of evidence (WoE).vtt 4.6 kB
4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.vtt 4.6 kB
6. Categorical Variable Encoding/9. Count encoding Demo.vtt 4.5 kB
10. Feature Scaling/4. Mean normalisation.vtt 4.5 kB
8. Discretisation/2. Equal-width discretisation.srt 4.5 kB
9. Outlier Handling/4. Outlier capping with mean and std.vtt 4.4 kB
4. Missing Data Imputation/7. Missing category imputation.vtt 4.4 kB
8. Discretisation/6. K-means discretisation.vtt 4.3 kB
8. Discretisation/4. Equal-frequency discretisation.vtt 4.3 kB
4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.srt 4.2 kB
4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.vtt 4.2 kB
1. Introduction/3. Course requirements.srt 4.2 kB
8. Discretisation/13. Domain knowledge discretisation.srt 4.2 kB
10. Feature Scaling/1. Feature scaling Introduction.vtt 4.2 kB
2. Variable Types/3. Categorical variables.vtt 4.2 kB
10. Feature Scaling/9. MaxAbsScaling Demo.vtt 4.1 kB
7. Variable Transformation/4. Variable transformation with Feature-engine.srt 4.1 kB
11. Engineering mixed variables/1. Engineering mixed variables.srt 4.1 kB
4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.srt 4.0 kB
8. Discretisation/2. Equal-width discretisation.vtt 4.0 kB
8. Discretisation/12. Discretisation with decision trees using Feature-engine.srt 4.0 kB
3. Variable Characteristics/8. Variable magnitude.srt 4.0 kB
9. Outlier Handling/6. Arbitrary capping.srt 3.9 kB
10. Feature Scaling/6. Scaling to minimum and maximum values.srt 3.9 kB
6. Categorical Variable Encoding/8. Count or frequency encoding.srt 3.8 kB
4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.vtt 3.8 kB
1. Introduction/3. Course requirements.vtt 3.7 kB
8. Discretisation/13. Domain knowledge discretisation.vtt 3.7 kB
7. Variable Transformation/4. Variable transformation with Feature-engine.vtt 3.7 kB
3. Variable Characteristics/1. Variable characteristics.srt 3.7 kB
11. Engineering mixed variables/1. Engineering mixed variables.vtt 3.6 kB
2. Variable Types/1. Variables Intro.srt 3.6 kB
1. Introduction/5. Setting up your computer.html 3.6 kB
9. Outlier Handling/5. Outlier capping with quantiles.srt 3.6 kB
8. Discretisation/12. Discretisation with decision trees using Feature-engine.vtt 3.6 kB
10. Feature Scaling/7. MinMaxScaling Demo.srt 3.6 kB
4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.vtt 3.6 kB
3. Variable Characteristics/8. Variable magnitude.vtt 3.5 kB
6. Categorical Variable Encoding/10. Target guided ordinal encoding.srt 3.5 kB
9. Outlier Handling/6. Arbitrary capping.vtt 3.5 kB
8. Discretisation/1. Discretisation Introduction.srt 3.5 kB
10. Feature Scaling/6. Scaling to minimum and maximum values.vtt 3.5 kB
6. Categorical Variable Encoding/8. Count or frequency encoding.vtt 3.4 kB
6. Categorical Variable Encoding/4. One hot encoding of top categories.srt 3.4 kB
10. Feature Scaling/8. Maximum absolute scaling.srt 3.4 kB
4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.srt 3.3 kB
8. Discretisation/7. K-means discretisation Demo.srt 3.3 kB
3. Variable Characteristics/1. Variable characteristics.vtt 3.3 kB
9. Outlier Handling/5. Outlier capping with quantiles.vtt 3.2 kB
10. Feature Scaling/10. Scaling to median and quantiles.srt 3.2 kB
2. Variable Types/1. Variables Intro.vtt 3.2 kB
10. Feature Scaling/7. MinMaxScaling Demo.vtt 3.2 kB
6. Categorical Variable Encoding/10. Target guided ordinal encoding.vtt 3.1 kB
8. Discretisation/1. Discretisation Introduction.vtt 3.1 kB
4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.srt 3.1 kB
6. Categorical Variable Encoding/4. One hot encoding of top categories.vtt 3.0 kB
2. Variable Types/5. Mixed variables.srt 3.0 kB
10. Feature Scaling/8. Maximum absolute scaling.vtt 3.0 kB
6. Categorical Variable Encoding/12. Mean encoding.srt 3.0 kB
4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.vtt 3.0 kB
8. Discretisation/7. K-means discretisation Demo.vtt 2.9 kB
10. Feature Scaling/10. Scaling to median and quantiles.vtt 2.9 kB
8. Discretisation/8. Discretisation plus categorical encoding.srt 2.8 kB
4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.vtt 2.8 kB
4. Missing Data Imputation/25. Conclusion when to use each missing data imputation method.html 2.7 kB
6. Categorical Variable Encoding/12. Mean encoding.vtt 2.7 kB
2. Variable Types/5. Mixed variables.vtt 2.7 kB
4. Missing Data Imputation/21. Missing category imputation with Feature-engine.srt 2.6 kB
8. Discretisation/8. Discretisation plus categorical encoding.vtt 2.5 kB
10. Feature Scaling/11. Robust Scaling Demo.srt 2.5 kB
6. Categorical Variable Encoding/21. Bonus Additional reading resources.html 2.5 kB
2. Variable Types/4. Date and time variables.srt 2.5 kB
4. Missing Data Imputation/22. Random sample imputation with Feature-engine.srt 2.4 kB
4. Missing Data Imputation/21. Missing category imputation with Feature-engine.vtt 2.3 kB
10. Feature Scaling/11. Robust Scaling Demo.vtt 2.2 kB
2. Variable Types/4. Date and time variables.vtt 2.2 kB
6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.srt 2.1 kB
4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.srt 2.1 kB
4. Missing Data Imputation/22. Random sample imputation with Feature-engine.vtt 2.1 kB
1. Introduction/7. Download datasets.html 2.0 kB
6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.vtt 1.9 kB
4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.vtt 1.9 kB
1. Introduction/4. How to approach this course.html 1.8 kB
1. Introduction/9. FAQ Data Science, Python programming, datasets, presentations and more....html 1.7 kB
8. Discretisation/14. Bonus Additional reading resources.html 1.4 kB
10. Feature Scaling/14. Additional reading resources.html 1.4 kB
1. Introduction/6. Download Jupyter notebooks.html 1.3 kB
8. Discretisation/14.1 15.5_Bonus_Additional_reading_resources.zip.zip 1.1 kB
14. Final section Next steps/1. BONUS Discounts on my other courses!.html 1.0 kB
2. Variable Types/6. Bonus More about the Lending Club dataset.html 826 Bytes
3. Variable Characteristics/11. FAQ How can I learn more about machine learning.html 824 Bytes
1. Introduction/8. Download course presentations.html 764 Bytes
9. Outlier Handling/7. Additional reading resources.html 387 Bytes
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13. Assembling a feature engineering pipeline/3. Beat the performance by engineering features.html 155 Bytes
2. Variable Types/7. Quiz about variable types.html 151 Bytes
3. Variable Characteristics/9. Bonus Machine learning algorithms overview.html 140 Bytes
4. Missing Data Imputation/24. Overview of missing value imputation methods.html 140 Bytes
5. Multivariate Missing Data Imputation/1. Multivariate Imputation - COMING IN 2020.html 105 Bytes

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