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

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2019-12-3 23:30 2024-12-7 23:32 162 3.71 GB 102
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迅雷链接
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FreeTutorials Us Udemy - Feature Engineering for Machine Learning

文件列表
  1. 1. Introduction/1. Introduction.mp432.86MB
  2. 1. Introduction/2. Course curriculum overview.mp433.37MB
  3. 1. Introduction/3. Course requirements.mp410.64MB
  4. 10. Feature Scaling/1. Feature scaling Introduction.mp420.6MB
  5. 10. Feature Scaling/10. Scaling to median and quantiles.mp413.01MB
  6. 10. Feature Scaling/11. Robust Scaling Demo.mp416.55MB
  7. 10. Feature Scaling/12. Scaling to vector unit length.mp431.94MB
  8. 10. Feature Scaling/13. Scaling to vector unit length Demo.mp446.31MB
  9. 10. Feature Scaling/2. Standardisation.mp426.51MB
  10. 10. Feature Scaling/3. Standardisation Demo.mp441.62MB
  11. 10. Feature Scaling/4. Mean normalisation.mp419.81MB
  12. 10. Feature Scaling/5. Mean normalisation Demo.mp445.08MB
  13. 10. Feature Scaling/6. Scaling to minimum and maximum values.mp417.08MB
  14. 10. Feature Scaling/7. MinMaxScaling Demo.mp425.89MB
  15. 10. Feature Scaling/8. Maximum absolute scaling.mp414.6MB
  16. 10. Feature Scaling/9. MaxAbsScaling Demo.mp431.47MB
  17. 11. Engineering mixed variables/1. Engineering mixed variables.mp415.27MB
  18. 11. Engineering mixed variables/2. Engineering mixed variables Demo.mp445.48MB
  19. 12. Engineering datetime variables/1. Engineering datetime variables.mp423.19MB
  20. 12. Engineering datetime variables/2. Engineering dates Demo.mp454.01MB
  21. 12. Engineering datetime variables/3. Engineering time variables and different timezones.mp433.48MB
  22. 13. Assembling a feature engineering pipeline/1. Classification pipeline.mp4135.99MB
  23. 13. Assembling a feature engineering pipeline/2. Regression pipeline.mp4157.57MB
  24. 2. Variable Types/1. Variables Intro.mp415.3MB
  25. 2. Variable Types/2. Numerical variables.mp426.88MB
  26. 2. Variable Types/3. Categorical variables.mp418.4MB
  27. 2. Variable Types/4. Date and time variables.mp49.8MB
  28. 2. Variable Types/5. Mixed variables.mp411.25MB
  29. 3. Variable Characteristics/1. Variable characteristics.mp420.84MB
  30. 3. Variable Characteristics/2. Missing data.mp440.11MB
  31. 3. Variable Characteristics/3. Cardinality - categorical variables.mp431.02MB
  32. 3. Variable Characteristics/4. Rare Labels - categorical variables.mp433.86MB
  33. 3. Variable Characteristics/5. Linear models assumptions.mp468.89MB
  34. 3. Variable Characteristics/6. Variable distribution.mp432.77MB
  35. 3. Variable Characteristics/7. Outliers.mp448.36MB
  36. 3. Variable Characteristics/8. Variable magnitude.mp419.96MB
  37. 4. Missing Data Imputation/1. Introduction to missing data imputation.mp429.37MB
  38. 4. Missing Data Imputation/10. Mean or median imputation with Scikit-learn.mp488.12MB
  39. 4. Missing Data Imputation/11. Arbitrary value imputation with Scikit-learn.mp452.16MB
  40. 4. Missing Data Imputation/12. Frequent category imputation with Scikit-learn.mp434.18MB
  41. 4. Missing Data Imputation/13. Missing category imputation with Scikit-learn.mp424.61MB
  42. 4. Missing Data Imputation/14. Adding a missing indicator with Scikit-learn.mp435.67MB
  43. 4. Missing Data Imputation/15. Automatic determination of imputation method with Sklearn.mp480.35MB
  44. 4. Missing Data Imputation/16. Introduction to Feature-engine.mp440.48MB
  45. 4. Missing Data Imputation/17. Mean or median imputation with Feature-engine.mp438.64MB
  46. 4. Missing Data Imputation/18. Arbitrary value imputation with Feature-engine.mp426.75MB
  47. 4. Missing Data Imputation/19. End of distribution imputation with Feature-engine.mp438.87MB
  48. 4. Missing Data Imputation/2. Complete Case Analysis.mp446.67MB
  49. 4. Missing Data Imputation/20. Frequent category imputation with Feature-engine.mp416.15MB
  50. 4. Missing Data Imputation/21. Missing category imputation with Feature-engine.mp420.42MB
  51. 4. Missing Data Imputation/22. Random sample imputation with Feature-engine.mp416.09MB
  52. 4. Missing Data Imputation/23. Adding a missing indicator with Feature-engine.mp425.9MB
  53. 4. Missing Data Imputation/3. Mean or median imputation.mp452.15MB
  54. 4. Missing Data Imputation/4. Arbitrary value imputation.mp440.09MB
  55. 4. Missing Data Imputation/5. End of distribution imputation.mp428.11MB
  56. 4. Missing Data Imputation/6. Frequent category imputation.mp449.77MB
  57. 4. Missing Data Imputation/7. Missing category imputation.mp428.17MB
  58. 4. Missing Data Imputation/8. Random sample imputation.mp4102.66MB
  59. 4. Missing Data Imputation/9. Adding a missing indicator.mp431.09MB
  60. 6. Categorical Variable Encoding/1. Categorical encoding Introduction.mp434.03MB
  61. 6. Categorical Variable Encoding/10. Target guided ordinal encoding.mp412.87MB
  62. 6. Categorical Variable Encoding/11. Target guided ordinal encoding Demo.mp468.75MB
  63. 6. Categorical Variable Encoding/12. Mean encoding.mp412.84MB
  64. 6. Categorical Variable Encoding/13. Mean encoding Demo.mp442.05MB
  65. 6. Categorical Variable Encoding/14. Probability ratio encoding.mp445.65MB
  66. 6. Categorical Variable Encoding/15. Weight of evidence (WoE).mp420.56MB
  67. 6. Categorical Variable Encoding/16. Weight of Evidence Demo.mp445.11MB
  68. 6. Categorical Variable Encoding/17. Comparison of categorical variable encoding.mp478.44MB
  69. 6. Categorical Variable Encoding/18. Rare label encoding.mp423.31MB
  70. 6. Categorical Variable Encoding/19. Rare label encoding Demo.mp469.43MB
  71. 6. Categorical Variable Encoding/2. One hot encoding.mp431.75MB
  72. 6. Categorical Variable Encoding/20. Binary encoding and feature hashing.mp430.9MB
  73. 6. Categorical Variable Encoding/3. One-hot-encoding Demo.mp491.4MB
  74. 6. Categorical Variable Encoding/4. One hot encoding of top categories.mp418.1MB
  75. 6. Categorical Variable Encoding/5. One hot encoding of top categories Demo.mp457.26MB
  76. 6. Categorical Variable Encoding/6. Ordinal encoding Label encoding.mp49.42MB
  77. 6. Categorical Variable Encoding/7. Ordinal encoding Demo.mp457.48MB
  78. 6. Categorical Variable Encoding/8. Count or frequency encoding.mp415.73MB
  79. 6. Categorical Variable Encoding/9. Count encoding Demo.mp432.53MB
  80. 7. Variable Transformation/1. Variable Transformation Introduction.mp418.66MB
  81. 7. Variable Transformation/2. Variable Transformation with Numpy and SciPy.mp449.41MB
  82. 7. Variable Transformation/3. variable Transformation with Scikit-learn.mp447.1MB
  83. 7. Variable Transformation/4. Variable transformation with Feature-engine.mp423.69MB
  84. 8. Discretisation/1. Discretisation Introduction.mp415.45MB
  85. 8. Discretisation/10. Discretisation with classification trees.mp426.58MB
  86. 8. Discretisation/11. Discretisation with decision trees using Scikit-learn.mp480.16MB
  87. 8. Discretisation/12. Discretisation with decision trees using Feature-engine.mp428.38MB
  88. 8. Discretisation/13. Domain knowledge discretisation.mp425.67MB
  89. 8. Discretisation/2. Equal-width discretisation.mp421.54MB
  90. 8. Discretisation/3. Equal-width discretisation Demo.mp479.1MB
  91. 8. Discretisation/4. Equal-frequency discretisation.mp422.49MB
  92. 8. Discretisation/5. Equal-frequency discretisation Demo.mp447.29MB
  93. 8. Discretisation/6. K-means discretisation.mp418.87MB
  94. 8. Discretisation/7. K-means discretisation Demo.mp418.83MB
  95. 8. Discretisation/8. Discretisation plus categorical encoding.mp413.31MB
  96. 8. Discretisation/9. Discretisation plus encoding Demo.mp436.22MB
  97. 9. Outlier Handling/1. Outlier Engineering Intro.mp441.97MB
  98. 9. Outlier Handling/2. Outlier trimming.mp451.09MB
  99. 9. Outlier Handling/3. Outlier capping with IQR.mp443.57MB
  100. 9. Outlier Handling/4. Outlier capping with mean and std.mp434.58MB
  101. 9. Outlier Handling/5. Outlier capping with quantiles.mp424.44MB
  102. 9. Outlier Handling/6. Arbitrary capping.mp419.69MB
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