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[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
magnet:?xt=urn:btih:c4069cac192c286f32cbe87a76ff1ddc6f293ea8&dn=[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
磁力链接详情
Hash值:
c4069cac192c286f32cbe87a76ff1ddc6f293ea8
点击数:
141
文件大小:
3.71 GB
文件数量:
102
创建日期:
2019-12-3 23:30
最后访问:
2024-9-7 05:01
访问标签:
FreeTutorials
Us
Udemy
-
Feature
Engineering
for
Machine
Learning
文件列表详情
1. Introduction/1. Introduction.mp4 32.86 MB
1. Introduction/2. Course curriculum overview.mp4 33.37 MB
1. Introduction/3. Course requirements.mp4 10.64 MB
10. Feature Scaling/1. Feature scaling Introduction.mp4 20.6 MB
10. Feature Scaling/10. Scaling to median and quantiles.mp4 13.01 MB
10. Feature Scaling/11. Robust Scaling Demo.mp4 16.55 MB
10. Feature Scaling/12. Scaling to vector unit length.mp4 31.94 MB
10. Feature Scaling/13. Scaling to vector unit length Demo.mp4 46.31 MB
10. Feature Scaling/2. Standardisation.mp4 26.51 MB
10. Feature Scaling/3. Standardisation Demo.mp4 41.62 MB
10. Feature Scaling/4. Mean normalisation.mp4 19.81 MB
10. Feature Scaling/5. Mean normalisation Demo.mp4 45.08 MB
10. Feature Scaling/6. Scaling to minimum and maximum values.mp4 17.08 MB
10. Feature Scaling/7. MinMaxScaling Demo.mp4 25.89 MB
10. Feature Scaling/8. Maximum absolute scaling.mp4 14.6 MB
10. Feature Scaling/9. MaxAbsScaling Demo.mp4 31.47 MB
11. Engineering mixed variables/1. Engineering mixed variables.mp4 15.27 MB
11. Engineering mixed variables/2. Engineering mixed variables Demo.mp4 45.48 MB
12. Engineering datetime variables/1. Engineering datetime variables.mp4 23.19 MB
12. Engineering datetime variables/2. Engineering dates Demo.mp4 54.01 MB
12. Engineering datetime variables/3. Engineering time variables and different timezones.mp4 33.48 MB
13. Assembling a feature engineering pipeline/1. Classification pipeline.mp4 135.99 MB
13. Assembling a feature engineering pipeline/2. Regression pipeline.mp4 157.57 MB
2. Variable Types/1. Variables Intro.mp4 15.3 MB
2. Variable Types/2. Numerical variables.mp4 26.88 MB
2. Variable Types/3. Categorical variables.mp4 18.4 MB
2. Variable Types/4. Date and time variables.mp4 9.8 MB
2. Variable Types/5. Mixed variables.mp4 11.25 MB
3. Variable Characteristics/1. Variable characteristics.mp4 20.84 MB
3. Variable Characteristics/2. Missing data.mp4 40.11 MB
3. Variable Characteristics/3. Cardinality - categorical variables.mp4 31.02 MB
3. Variable Characteristics/4. Rare Labels - categorical variables.mp4 33.86 MB
3. Variable Characteristics/5. Linear models assumptions.mp4 68.89 MB
3. Variable Characteristics/6. Variable distribution.mp4 32.77 MB
3. Variable Characteristics/7. Outliers.mp4 48.36 MB
3. Variable Characteristics/8. Variable magnitude.mp4 19.96 MB
4. Missing Data Imputation/1. Introduction to missing data imputation.mp4 29.37 MB
4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.mp4 88.12 MB
4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.mp4 52.16 MB
4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.mp4 34.18 MB
4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.mp4 24.61 MB
4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.mp4 35.67 MB
4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.mp4 80.35 MB
4. Missing Data Imputation/16. Introduction to Feature-engine.mp4 40.48 MB
4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.mp4 38.64 MB
4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.mp4 26.75 MB
4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.mp4 38.87 MB
4. Missing Data Imputation/2. Complete Case Analysis.mp4 46.67 MB
4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.mp4 16.15 MB
4. Missing Data Imputation/21. Missing category imputation with Feature-engine.mp4 20.42 MB
4. Missing Data Imputation/22. Random sample imputation with Feature-engine.mp4 16.09 MB
4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.mp4 25.9 MB
4. Missing Data Imputation/3. Mean or median imputation.mp4 52.15 MB
4. Missing Data Imputation/4. Arbitrary value imputation.mp4 40.09 MB
4. Missing Data Imputation/5. End of distribution imputation.mp4 28.11 MB
4. Missing Data Imputation/6. Frequent category imputation.mp4 49.77 MB
4. Missing Data Imputation/7. Missing category imputation.mp4 28.17 MB
4. Missing Data Imputation/8. Random sample imputation.mp4 102.66 MB
4. Missing Data Imputation/9. Adding a missing indicator.mp4 31.09 MB
6. Categorical Variable Encoding/1. Categorical encoding Introduction.mp4 34.03 MB
6. Categorical Variable Encoding/10. Target guided ordinal encoding.mp4 12.87 MB
6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.mp4 68.75 MB
6. Categorical Variable Encoding/12. Mean encoding.mp4 12.84 MB
6. Categorical Variable Encoding/13. Mean encoding Demo.mp4 42.05 MB
6. Categorical Variable Encoding/14. Probability ratio encoding.mp4 45.65 MB
6. Categorical Variable Encoding/15. Weight of evidence (WoE).mp4 20.56 MB
6. Categorical Variable Encoding/16. Weight of Evidence Demo.mp4 45.11 MB
6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.mp4 78.44 MB
6. Categorical Variable Encoding/18. Rare label encoding.mp4 23.31 MB
6. Categorical Variable Encoding/19. Rare label encoding Demo.mp4 69.43 MB
6. Categorical Variable Encoding/2. One hot encoding.mp4 31.75 MB
6. Categorical Variable Encoding/20. Binary encoding and feature hashing.mp4 30.9 MB
6. Categorical Variable Encoding/3. One-hot-encoding Demo.mp4 91.4 MB
6. Categorical Variable Encoding/4. One hot encoding of top categories.mp4 18.1 MB
6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.mp4 57.26 MB
6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.mp4 9.42 MB
6. Categorical Variable Encoding/7. Ordinal encoding Demo.mp4 57.48 MB
6. Categorical Variable Encoding/8. Count or frequency encoding.mp4 15.73 MB
6. Categorical Variable Encoding/9. Count encoding Demo.mp4 32.53 MB
7. Variable Transformation/1. Variable Transformation Introduction.mp4 18.66 MB
7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.mp4 49.41 MB
7. Variable Transformation/3. variable Transformation with Scikit-learn.mp4 47.1 MB
7. Variable Transformation/4. Variable transformation with Feature-engine.mp4 23.69 MB
8. Discretisation/1. Discretisation Introduction.mp4 15.45 MB
8. Discretisation/10. Discretisation with classification trees.mp4 26.58 MB
8. Discretisation/11. Discretisation with decision trees using Scikit-learn.mp4 80.16 MB
8. Discretisation/12. Discretisation with decision trees using Feature-engine.mp4 28.38 MB
8. Discretisation/13. Domain knowledge discretisation.mp4 25.67 MB
8. Discretisation/2. Equal-width discretisation.mp4 21.54 MB
8. Discretisation/3. Equal-width discretisation Demo.mp4 79.1 MB
8. Discretisation/4. Equal-frequency discretisation.mp4 22.49 MB
8. Discretisation/5. Equal-frequency discretisation Demo.mp4 47.29 MB
8. Discretisation/6. K-means discretisation.mp4 18.87 MB
8. Discretisation/7. K-means discretisation Demo.mp4 18.83 MB
8. Discretisation/8. Discretisation plus categorical encoding.mp4 13.31 MB
8. Discretisation/9. Discretisation plus encoding Demo.mp4 36.22 MB
9. Outlier Handling/1. Outlier Engineering Intro.mp4 41.97 MB
9. Outlier Handling/2. Outlier trimming.mp4 51.09 MB
9. Outlier Handling/3. Outlier capping with IQR.mp4 43.57 MB
9. Outlier Handling/4. Outlier capping with mean and std.mp4 34.58 MB
9. Outlier Handling/5. Outlier capping with quantiles.mp4 24.44 MB
9. Outlier Handling/6. Arbitrary capping.mp4 19.69 MB
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