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种子名称： [FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
文件类型： 视频
文件数目： 102个文件
文件大小： 3.71 GB
收录时间： 2019-12-3 23:30
已经下载： 3次
资源热度： 128
最近下载： 2024-4-23 19:35

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[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning.torrent

1. Introduction/1. Introduction.mp432.86MB

1. Introduction/2. Course curriculum overview.mp433.37MB

1. Introduction/3. Course requirements.mp410.64MB

10. Feature Scaling/1. Feature scaling Introduction.mp420.6MB

10. Feature Scaling/10. Scaling to median and quantiles.mp413.01MB

10. Feature Scaling/11. Robust Scaling Demo.mp416.55MB

10. Feature Scaling/12. Scaling to vector unit length.mp431.94MB

10. Feature Scaling/13. Scaling to vector unit length Demo.mp446.31MB

10. Feature Scaling/2. Standardisation.mp426.51MB

10. Feature Scaling/3. Standardisation Demo.mp441.62MB

10. Feature Scaling/4. Mean normalisation.mp419.81MB

10. Feature Scaling/5. Mean normalisation Demo.mp445.08MB

10. Feature Scaling/6. Scaling to minimum and maximum values.mp417.08MB

10. Feature Scaling/7. MinMaxScaling Demo.mp425.89MB

10. Feature Scaling/8. Maximum absolute scaling.mp414.6MB

10. Feature Scaling/9. MaxAbsScaling Demo.mp431.47MB

11. Engineering mixed variables/1. Engineering mixed variables.mp415.27MB

11. Engineering mixed variables/2. Engineering mixed variables Demo.mp445.48MB

12. Engineering datetime variables/1. Engineering datetime variables.mp423.19MB

12. Engineering datetime variables/2. Engineering dates Demo.mp454.01MB

12. Engineering datetime variables/3. Engineering time variables and different timezones.mp433.48MB

13. Assembling a feature engineering pipeline/1. Classification pipeline.mp4135.99MB

13. Assembling a feature engineering pipeline/2. Regression pipeline.mp4157.57MB

2. Variable Types/1. Variables Intro.mp415.3MB

2. Variable Types/2. Numerical variables.mp426.88MB

2. Variable Types/3. Categorical variables.mp418.4MB

2. Variable Types/4. Date and time variables.mp49.8MB

2. Variable Types/5. Mixed variables.mp411.25MB

3. Variable Characteristics/1. Variable characteristics.mp420.84MB

3. Variable Characteristics/2. Missing data.mp440.11MB

3. Variable Characteristics/3. Cardinality - categorical variables.mp431.02MB

3. Variable Characteristics/4. Rare Labels - categorical variables.mp433.86MB

3. Variable Characteristics/5. Linear models assumptions.mp468.89MB

3. Variable Characteristics/6. Variable distribution.mp432.77MB

3. Variable Characteristics/7. Outliers.mp448.36MB

3. Variable Characteristics/8. Variable magnitude.mp419.96MB

4. Missing Data Imputation/1. Introduction to missing data imputation.mp429.37MB

4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.mp488.12MB

4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.mp452.16MB

4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.mp434.18MB

4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.mp424.61MB

4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.mp435.67MB

4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.mp480.35MB

4. Missing Data Imputation/16. Introduction to Feature-engine.mp440.48MB

4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.mp438.64MB

4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.mp426.75MB

4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.mp438.87MB

4. Missing Data Imputation/2. Complete Case Analysis.mp446.67MB

4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.mp416.15MB

4. Missing Data Imputation/21. Missing category imputation with Feature-engine.mp420.42MB

4. Missing Data Imputation/22. Random sample imputation with Feature-engine.mp416.09MB

4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.mp425.9MB

4. Missing Data Imputation/3. Mean or median imputation.mp452.15MB

4. Missing Data Imputation/4. Arbitrary value imputation.mp440.09MB

4. Missing Data Imputation/5. End of distribution imputation.mp428.11MB

4. Missing Data Imputation/6. Frequent category imputation.mp449.77MB

4. Missing Data Imputation/7. Missing category imputation.mp428.17MB

4. Missing Data Imputation/8. Random sample imputation.mp4102.66MB

4. Missing Data Imputation/9. Adding a missing indicator.mp431.09MB

6. Categorical Variable Encoding/1. Categorical encoding Introduction.mp434.03MB

6. Categorical Variable Encoding/10. Target guided ordinal encoding.mp412.87MB

6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.mp468.75MB

6. Categorical Variable Encoding/12. Mean encoding.mp412.84MB

6. Categorical Variable Encoding/13. Mean encoding Demo.mp442.05MB

6. Categorical Variable Encoding/14. Probability ratio encoding.mp445.65MB

6. Categorical Variable Encoding/15. Weight of evidence (WoE).mp420.56MB

6. Categorical Variable Encoding/16. Weight of Evidence Demo.mp445.11MB

6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.mp478.44MB

6. Categorical Variable Encoding/18. Rare label encoding.mp423.31MB

6. Categorical Variable Encoding/19. Rare label encoding Demo.mp469.43MB

6. Categorical Variable Encoding/2. One hot encoding.mp431.75MB

6. Categorical Variable Encoding/20. Binary encoding and feature hashing.mp430.9MB

6. Categorical Variable Encoding/3. One-hot-encoding Demo.mp491.4MB

6. Categorical Variable Encoding/4. One hot encoding of top categories.mp418.1MB

6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.mp457.26MB

6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.mp49.42MB

6. Categorical Variable Encoding/7. Ordinal encoding Demo.mp457.48MB

6. Categorical Variable Encoding/8. Count or frequency encoding.mp415.73MB

6. Categorical Variable Encoding/9. Count encoding Demo.mp432.53MB

7. Variable Transformation/1. Variable Transformation Introduction.mp418.66MB

7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.mp449.41MB

7. Variable Transformation/3. variable Transformation with Scikit-learn.mp447.1MB

7. Variable Transformation/4. Variable transformation with Feature-engine.mp423.69MB

8. Discretisation/1. Discretisation Introduction.mp415.45MB

8. Discretisation/10. Discretisation with classification trees.mp426.58MB

8. Discretisation/11. Discretisation with decision trees using Scikit-learn.mp480.16MB

8. Discretisation/12. Discretisation with decision trees using Feature-engine.mp428.38MB

8. Discretisation/13. Domain knowledge discretisation.mp425.67MB

8. Discretisation/2. Equal-width discretisation.mp421.54MB

8. Discretisation/3. Equal-width discretisation Demo.mp479.1MB

8. Discretisation/4. Equal-frequency discretisation.mp422.49MB

8. Discretisation/5. Equal-frequency discretisation Demo.mp447.29MB

8. Discretisation/6. K-means discretisation.mp418.87MB

8. Discretisation/7. K-means discretisation Demo.mp418.83MB

8. Discretisation/8. Discretisation plus categorical encoding.mp413.31MB

8. Discretisation/9. Discretisation plus encoding Demo.mp436.22MB

9. Outlier Handling/1. Outlier Engineering Intro.mp441.97MB

9. Outlier Handling/2. Outlier trimming.mp451.09MB

9. Outlier Handling/3. Outlier capping with IQR.mp443.57MB

9. Outlier Handling/4. Outlier capping with mean and std.mp434.58MB

9. Outlier Handling/5. Outlier capping with quantiles.mp424.44MB

9. Outlier Handling/6. Arbitrary capping.mp419.69MB