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[ ] Udemy - Data pre-processing for Machine Learning in Python

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文件列表

  1. ~Get Your Files Here !/9. A complete pipeline/1. An example of a complete pipeline.mp4 121MB
  2. ~Get Your Files Here !/3. Encoding of the categorical features/2. One-hot encoding.mp4 115MB
  3. ~Get Your Files Here !/2. Data cleaning/6. ColumnTransformer and make_column_selector.mp4 88MB
  4. ~Get Your Files Here !/8. Filter-based feature selection/6. Feature importance according to a model.mp4 87MB
  5. ~Get Your Files Here !/2. Data cleaning/7. Exercises.mp4 81MB
  6. ~Get Your Files Here !/5. Pipelines/3. Exercises.mp4 79MB
  7. ~Get Your Files Here !/5. Pipelines/2. Pipelines and ColumnTransformer together.mp4 79MB
  8. ~Get Your Files Here !/8. Filter-based feature selection/2. Numerical features, numerical target.mp4 78MB
  9. ~Get Your Files Here !/4. Transformations of the numerical features/6. Exercise.mp4 77MB
  10. ~Get Your Files Here !/3. Encoding of the categorical features/5. Exercise.mp4 74MB
  11. ~Get Your Files Here !/6. Scaling/2. Normalization, Standardization, Robust scaling.mp4 71MB
  12. ~Get Your Files Here !/8. Filter-based feature selection/4. Categorical features, numerical target.mp4 71MB
  13. ~Get Your Files Here !/7. Principal Component Analysis/2. How to perform PCA.mp4 62MB
  14. ~Get Your Files Here !/2. Data cleaning/5. KNN blank filling.mp4 61MB
  15. ~Get Your Files Here !/4. Transformations of the numerical features/3. Binning.mp4 60MB
  16. ~Get Your Files Here !/2. Data cleaning/3. Cleaning the numerical features.mp4 59MB
  17. ~Get Your Files Here !/10. Oversampling/2. How to perform SMOTE.mp4 57MB
  18. ~Get Your Files Here !/8. Filter-based feature selection/5. Categorical features, categorical target.mp4 57MB
  19. ~Get Your Files Here !/8. Filter-based feature selection/9. Exercises.mp4 54MB
  20. ~Get Your Files Here !/8. Filter-based feature selection/3. Numerical features, categorical target.mp4 52MB
  21. ~Get Your Files Here !/6. Scaling/3. Exercise.mp4 51MB
  22. ~Get Your Files Here !/4. Transformations of the numerical features/2. Power Transformation.mp4 49MB
  23. ~Get Your Files Here !/4. Transformations of the numerical features/5. Applying an arbitrary transformation.mp4 42MB
  24. ~Get Your Files Here !/3. Encoding of the categorical features/3. Ordinal encoding.mp4 40MB
  25. ~Get Your Files Here !/5. Pipelines/1. Define a transformation pipeline.mp4 39MB
  26. ~Get Your Files Here !/10. Oversampling/3. Exercise.mp4 35MB
  27. ~Get Your Files Here !/1. Introduction/5. Jupyter notebooks.mp4 35MB
  28. ~Get Your Files Here !/7. Principal Component Analysis/3. Exercise.mp4 33MB
  29. ~Get Your Files Here !/8. Filter-based feature selection/1. Introduction to feature selection.mp4 29MB
  30. ~Get Your Files Here !/2. Data cleaning/2. Selecting numerical and categorical variables.mp4 28MB
  31. ~Get Your Files Here !/10. Oversampling/1. Introduction to SMOTE.mp4 20MB
  32. ~Get Your Files Here !/6. Scaling/1. Introduction to scaling.mp4 19MB
  33. ~Get Your Files Here !/7. Principal Component Analysis/1. Introduction to PCA.mp4 19MB
  34. ~Get Your Files Here !/1. Introduction/1. Introduction to the course.mp4 18MB
  35. ~Get Your Files Here !/2. Data cleaning/4. Cleaning the categorical features.mp4 17MB
  36. ~Get Your Files Here !/4. Transformations of the numerical features/4. Binarizing.mp4 12MB
  37. ~Get Your Files Here !/1. Introduction/2. Numerical and categorical variables.mp4 12MB
  38. ~Get Your Files Here !/4. Transformations of the numerical features/1. Introduction to transformations.mp4 11MB
  39. ~Get Your Files Here !/3. Encoding of the categorical features/4. Label encoding of the target variable.mp4 10MB
  40. ~Get Your Files Here !/2. Data cleaning/1. Introduction to data cleaning.mp4 10MB
  41. ~Get Your Files Here !/3. Encoding of the categorical features/1. Introduction to the encoding of categorical variables.mp4 5MB
  42. ~Get Your Files Here !/1. Introduction/3.2 sample_dataset.csv 97KB
  43. ~Get Your Files Here !/8. Filter-based feature selection/4.1 Categorical features numerical target.ipynb 44KB
  44. ~Get Your Files Here !/4. Transformations of the numerical features/2.1 Power Transform.ipynb 43KB
  45. ~Get Your Files Here !/8. Filter-based feature selection/5.1 Categorical features categorical target.ipynb 43KB
  46. ~Get Your Files Here !/8. Filter-based feature selection/2.1 Numerical target numerical feature.ipynb 41KB
  47. ~Get Your Files Here !/2. Data cleaning/4.1 Cleaning the categorical features.ipynb 34KB
  48. ~Get Your Files Here !/4. Transformations of the numerical features/3.1 Binning.ipynb 30KB
  49. ~Get Your Files Here !/8. Filter-based feature selection/6.1 Feature importance according to model.ipynb 26KB
  50. ~Get Your Files Here !/7. Principal Component Analysis/2.1 PCA.ipynb 25KB
  51. ~Get Your Files Here !/2. Data cleaning/7.1 Exercises.ipynb 24KB
  52. ~Get Your Files Here !/3. Encoding of the categorical features/2. One-hot encoding.srt 20KB
  53. ~Get Your Files Here !/9. A complete pipeline/1. An example of a complete pipeline.srt 18KB
  54. ~Get Your Files Here !/6. Scaling/2.1 Scaling techniques.ipynb 14KB
  55. ~Get Your Files Here !/4. Transformations of the numerical features/4.1 Binarizer.ipynb 13KB
  56. ~Get Your Files Here !/2. Data cleaning/6. ColumnTransformer and make_column_selector.srt 13KB
  57. ~Get Your Files Here !/8. Filter-based feature selection/3.1 Numerical features categorical target.ipynb 13KB
  58. ~Get Your Files Here !/3. Encoding of the categorical features/5. Exercise.srt 12KB
  59. ~Get Your Files Here !/4. Transformations of the numerical features/5.1 FunctionTransformer.ipynb 12KB
  60. ~Get Your Files Here !/6. Scaling/2. Normalization, Standardization, Robust scaling.srt 12KB
  61. ~Get Your Files Here !/7. Principal Component Analysis/3.1 Exercises.ipynb 11KB
  62. ~Get Your Files Here !/5. Pipelines/2. Pipelines and ColumnTransformer together.srt 11KB
  63. ~Get Your Files Here !/9. A complete pipeline/1.1 A complete pipeline.ipynb 11KB
  64. ~Get Your Files Here !/4. Transformations of the numerical features/3. Binning.srt 11KB
  65. ~Get Your Files Here !/3. Encoding of the categorical features/2.1 One-hot encoding.ipynb 11KB
  66. ~Get Your Files Here !/8. Filter-based feature selection/6. Feature importance according to a model.srt 11KB
  67. ~Get Your Files Here !/5. Pipelines/3. Exercises.srt 11KB
  68. ~Get Your Files Here !/2. Data cleaning/5. KNN blank filling.srt 11KB
  69. ~Get Your Files Here !/2. Data cleaning/3. Cleaning the numerical features.srt 11KB
  70. ~Get Your Files Here !/10. Oversampling/2. How to perform SMOTE.srt 10KB
  71. ~Get Your Files Here !/4. Transformations of the numerical features/6. Exercise.srt 10KB
  72. ~Get Your Files Here !/8. Filter-based feature selection/2. Numerical features, numerical target.srt 9KB
  73. ~Get Your Files Here !/2. Data cleaning/7. Exercises.srt 9KB
  74. ~Get Your Files Here !/1. Introduction/5. Jupyter notebooks.srt 9KB
  75. ~Get Your Files Here !/5. Pipelines/1. Define a transformation pipeline.srt 9KB
  76. ~Get Your Files Here !/8. Filter-based feature selection/4. Categorical features, numerical target.srt 9KB
  77. ~Get Your Files Here !/4. Transformations of the numerical features/6.1 Exercises.ipynb 9KB
  78. ~Get Your Files Here !/10. Oversampling/2.1 How to do SMOTE.ipynb 9KB
  79. ~Get Your Files Here !/4. Transformations of the numerical features/2. Power Transformation.srt 9KB
  80. ~Get Your Files Here !/7. Principal Component Analysis/2. How to perform PCA.srt 9KB
  81. ~Get Your Files Here !/1. Introduction/3.1 sample_dataset_bins.csv 8KB
  82. ~Get Your Files Here !/8. Filter-based feature selection/9. Exercises.srt 8KB
  83. ~Get Your Files Here !/3. Encoding of the categorical features/3. Ordinal encoding.srt 8KB
  84. ~Get Your Files Here !/2. Data cleaning/3.1 Cleaning the numerical features.ipynb 8KB
  85. ~Get Your Files Here !/4. Transformations of the numerical features/5. Applying an arbitrary transformation.srt 7KB
  86. ~Get Your Files Here !/8. Filter-based feature selection/1. Introduction to feature selection.srt 7KB
  87. ~Get Your Files Here !/2. Data cleaning/6.1 ColumnTransformer.ipynb 7KB
  88. ~Get Your Files Here !/8. Filter-based feature selection/5. Categorical features, categorical target.srt 7KB
  89. ~Get Your Files Here !/2. Data cleaning/5.1 Cleaning with KNN.ipynb 7KB
  90. ~Get Your Files Here !/6. Scaling/3. Exercise.srt 7KB
  91. ~Get Your Files Here !/5. Pipelines/3.1 Exercises.ipynb 6KB
  92. ~Get Your Files Here !/7. Principal Component Analysis/3. Exercise.srt 6KB
  93. ~Get Your Files Here !/8. Filter-based feature selection/3. Numerical features, categorical target.srt 6KB
  94. ~Get Your Files Here !/10. Oversampling/3. Exercise.srt 6KB
  95. ~Get Your Files Here !/5. Pipelines/2.1 Pipelines and ColumnTransformer together .ipynb 6KB
  96. ~Get Your Files Here !/10. Oversampling/1. Introduction to SMOTE.srt 5KB
  97. ~Get Your Files Here !/3. Encoding of the categorical features/5.1 Exercises.ipynb 5KB
  98. ~Get Your Files Here !/10. Oversampling/3.1 Exercises.ipynb 5KB
  99. ~Get Your Files Here !/8. Filter-based feature selection/9.1 Exercises.ipynb 5KB
  100. ~Get Your Files Here !/2. Data cleaning/2.1 Select numerical and categorical variables.ipynb 5KB
  101. ~Get Your Files Here !/6. Scaling/3.1 Exercise.ipynb 4KB
  102. ~Get Your Files Here !/5. Pipelines/1.1 Define a transformation pipeline.ipynb 4KB
  103. ~Get Your Files Here !/2. Data cleaning/2. Selecting numerical and categorical variables.srt 4KB
  104. ~Get Your Files Here !/7. Principal Component Analysis/1. Introduction to PCA.srt 4KB
  105. ~Get Your Files Here !/2. Data cleaning/4. Cleaning the categorical features.srt 4KB
  106. ~Get Your Files Here !/3. Encoding of the categorical features/3.1 OrdinalEncoder.ipynb 4KB
  107. ~Get Your Files Here !/1. Introduction/1. Introduction to the course.srt 3KB
  108. ~Get Your Files Here !/6. Scaling/1. Introduction to scaling.srt 3KB
  109. ~Get Your Files Here !/4. Transformations of the numerical features/1. Introduction to transformations.srt 3KB
  110. ~Get Your Files Here !/3. Encoding of the categorical features/4. Label encoding of the target variable.srt 2KB
  111. ~Get Your Files Here !/4. Transformations of the numerical features/4. Binarizing.srt 2KB
  112. ~Get Your Files Here !/2. Data cleaning/1. Introduction to data cleaning.srt 2KB
  113. ~Get Your Files Here !/1. Introduction/2. Numerical and categorical variables.srt 2KB
  114. ~Get Your Files Here !/3. Encoding of the categorical features/4.1 LabelEncoder.ipynb 2KB
  115. ~Get Your Files Here !/11. General guidelines/1. Practical suggestions.html 1KB
  116. ~Get Your Files Here !/3. Encoding of the categorical features/1. Introduction to the encoding of categorical variables.srt 1KB
  117. ~Get Your Files Here !/8. Filter-based feature selection/7. A comment on mutual information.html 1KB
  118. ~Get Your Files Here !/4. Transformations of the numerical features/7. About power transformations.html 1KB
  119. ~Get Your Files Here !/8. Filter-based feature selection/8. A comment on feature selection with categorical variables.html 1013B
  120. ~Get Your Files Here !/1. Introduction/4. Required Python packages.html 919B
  121. ~Get Your Files Here !/Bonus Resources.txt 386B
  122. ~Get Your Files Here !/1. Introduction/3. The dataset.html 361B
  123. Get Bonus Downloads Here.url 182B