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[] Udemy - Feature Engineering for Machine Learning

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  • 文件大小:3GB
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文件列表

  1. 13 - Assembling a feature engineering pipeline/004 Regression pipeline.mp4 101MB
  2. 06 - Categorical Variable Encoding/017 Weight of Evidence Demo.mp4 98MB
  3. 04 - Missing Data Imputation/008 Random sample imputation.mp4 88MB
  4. 06 - Categorical Variable Encoding/004 One-hot-encoding Demo.mp4 86MB
  5. 13 - Assembling a feature engineering pipeline/003 Classification pipeline.mp4 77MB
  6. 06 - Categorical Variable Encoding/018 Comparison of categorical variable encoding.mp4 76MB
  7. 08 - Discretisation/012 Discretisation with decision trees using Scikit-learn.mp4 76MB
  8. 08 - Discretisation/004 Equal-width discretisation Demo.mp4 68MB
  9. 06 - Categorical Variable Encoding/012 Target guided ordinal encoding Demo.mp4 66MB
  10. 04 - Missing Data Imputation/016 Automatic determination of imputation method with Sklearn.mp4 65MB
  11. 06 - Categorical Variable Encoding/020 Rare label encoding Demo.mp4 61MB
  12. 13 - Assembling a feature engineering pipeline/005 Feature engineering pipeline with cross-validation.mp4 54MB
  13. 06 - Categorical Variable Encoding/006 One hot encoding of top categories Demo.mp4 54MB
  14. 01 - Introduction/001 Course curriculum overview.mp4 50MB
  15. 06 - Categorical Variable Encoding/008 Ordinal encoding Demo.mp4 50MB
  16. 10 - Feature Scaling/013 Scaling to vector unit length Demo.mp4 45MB
  17. 07 - Variable Transformation/003 Variable Transformation with Scikit-learn.mp4 44MB
  18. 10 - Feature Scaling/005 Mean normalisation Demo.mp4 43MB
  19. 07 - Variable Transformation/002 Variable Transformation with Numpy and SciPy.mp4 42MB
  20. 03 - Variable Characteristics/005 Linear models assumptions.mp4 41MB
  21. 09 - Outlier Handling/003 Outlier capping with IQR.mp4 41MB
  22. 08 - Discretisation/006 Equal-frequency discretisation Demo.mp4 41MB
  23. 10 - Feature Scaling/003 Standardisation Demo.mp4 40MB
  24. 12 - Engineering datetime variables/002 Engineering dates Demo.mp4 40MB
  25. 11 - Engineering mixed variables/002 Engineering mixed variables Demo.mp4 39MB
  26. 04 - Missing Data Imputation/002 Complete Case Analysis.mp4 39MB
  27. 04 - Missing Data Imputation/006 Frequent category imputation.mp4 38MB
  28. 04 - Missing Data Imputation/011 Mean or median imputation with Scikit-learn.mp4 38MB
  29. 09 - Outlier Handling/002 Outlier trimming.mp4 38MB
  30. 04 - Missing Data Imputation/025 CCA with Feature-engine.mp4 37MB
  31. 04 - Missing Data Imputation/012 Arbitrary value imputation with Scikit-learn.mp4 36MB
  32. 06 - Categorical Variable Encoding/014 Mean encoding Demo.mp4 36MB
  33. 04 - Missing Data Imputation/013 Frequent category imputation with Scikit-learn.mp4 35MB
  34. 06 - Categorical Variable Encoding/001 Categorical encoding Introduction.mp4 34MB
  35. 08 - Discretisation/010 Discretisation plus encoding Demo.mp4 34MB
  36. 13 - Assembling a feature engineering pipeline/001 Putting it all together.mp4 33MB
  37. 09 - Outlier Handling/001 Outlier Engineering Intro.mp4 32MB
  38. 04 - Missing Data Imputation/018 Mean or median imputation with Feature-engine.mp4 32MB
  39. 04 - Missing Data Imputation/004 Arbitrary value imputation.mp4 31MB
  40. 09 - Outlier Handling/004 Outlier capping with mean and std.mp4 30MB
  41. 04 - Missing Data Imputation/024 Adding a missing indicator with Feature-engine.mp4 28MB
  42. 05 - Multivariate Missing Data Imputation/006 MICE and missForest - Demo.mp4 28MB
  43. 10 - Feature Scaling/009 MaxAbsScaling Demo.mp4 27MB
  44. 04 - Missing Data Imputation/017 Introduction to Feature-engine.mp4 27MB
  45. 04 - Missing Data Imputation/020 End of distribution imputation with Feature-engine.mp4 26MB
  46. 04 - Missing Data Imputation/003 Mean or median imputation.mp4 26MB
  47. 04 - Missing Data Imputation/019 Arbitrary value imputation with Feature-engine.mp4 25MB
  48. 10 - Feature Scaling/007 MinMaxScaling Demo.mp4 25MB
  49. 08 - Discretisation/013 Discretisation with decision trees using Feature-engine.mp4 25MB
  50. 12 - Engineering datetime variables/003 Engineering time variables and different timezones.mp4 24MB
  51. 04 - Missing Data Imputation/007 Missing category imputation.mp4 23MB
  52. 04 - Missing Data Imputation/015 Adding a missing indicator with Scikit-learn.mp4 23MB
  53. 06 - Categorical Variable Encoding/015 Probability ratio encoding.mp4 23MB
  54. 03 - Variable Characteristics/003 Cardinality - categorical variables.mp4 22MB
  55. 13 - Assembling a feature engineering pipeline/002 Feature Engineering Pipeline.mp4 22MB
  56. 07 - Variable Transformation/004 Variable transformation with Feature-engine.mp4 22MB
  57. 03 - Variable Characteristics/002 Missing data.mp4 21MB
  58. 04 - Missing Data Imputation/010 Imputation with Scikit-learn.mp4 21MB
  59. 01 - Introduction/002 Course requirements.mp4 21MB
  60. 08 - Discretisation/011 Discretisation with classification trees.mp4 20MB
  61. 04 - Missing Data Imputation/014 Missing category imputation with Scikit-learn.mp4 20MB
  62. 04 - Missing Data Imputation/022 Missing category imputation with Feature-engine.mp4 20MB
  63. 05 - Multivariate Missing Data Imputation/003 KNN imputation - Demo.mp4 19MB
  64. 08 - Discretisation/014 Domain knowledge discretisation.mp4 19MB
  65. 03 - Variable Characteristics/008 Outliers.mp4 19MB
  66. 04 - Missing Data Imputation/005 End of distribution imputation.mp4 18MB
  67. 04 - Missing Data Imputation/001 Introduction to missing data imputation.mp4 18MB
  68. 04 - Missing Data Imputation/023 Random sample imputation with Feature-engine.mp4 17MB
  69. 06 - Categorical Variable Encoding/010 Count encoding Demo.mp4 17MB
  70. 08 - Discretisation/008 K-means discretisation Demo.mp4 16MB
  71. 10 - Feature Scaling/011 Robust Scaling Demo.mp4 16MB
  72. 08 - Discretisation/001 Discretisation Introduction.mp4 15MB
  73. 05 - Multivariate Missing Data Imputation/004 MICE.mp4 15MB
  74. 09 - Outlier Handling/006 Arbitrary capping.mp4 15MB
  75. 03 - Variable Characteristics/007 Variable distribution.mp4 15MB
  76. 02 - Variable Types/002 Numerical variables.mp4 15MB
  77. 04 - Missing Data Imputation/009 Adding a missing indicator.mp4 15MB
  78. 03 - Variable Characteristics/004 Rare labels - categorical variables.mp4 15MB
  79. 06 - Categorical Variable Encoding/021 Binary encoding and feature hashing.mp4 14MB
  80. 06 - Categorical Variable Encoding/002 One hot encoding.mp4 14MB
  81. 12 - Engineering datetime variables/001 Engineering datetime variables.mp4 13MB
  82. 10 - Feature Scaling/012 Scaling to vector unit length.mp4 13MB
  83. 11 - Engineering mixed variables/001 Engineering mixed variables.mp4 12MB
  84. 10 - Feature Scaling/002 Standardisation.mp4 12MB
  85. 09 - Outlier Handling/005 Outlier capping with quantiles.mp4 10MB
  86. 06 - Categorical Variable Encoding/019 Rare label encoding.mp4 10MB
  87. 06 - Categorical Variable Encoding/016 Weight of evidence (WoE).mp4 10MB
  88. 02 - Variable Types/005 sample-s2.csv 10MB
  89. 05 - Multivariate Missing Data Imputation/002 KNN imputation.mp4 10MB
  90. 08 - Discretisation/005 Equal-frequency discretisation.mp4 9MB
  91. 07 - Variable Transformation/001 Variable Transformation Introduction.mp4 9MB
  92. 10 - Feature Scaling/001 Feature scaling Introduction.mp4 9MB
  93. 06 - Categorical Variable Encoding/005 One hot encoding of top categories.mp4 9MB
  94. 08 - Discretisation/002 Equal-width discretisation.mp4 9MB
  95. 10 - Feature Scaling/004 Mean normalisation.mp4 9MB
  96. 08 - Discretisation/007 K-means discretisation.mp4 8MB
  97. 02 - Variable Types/003 Categorical variables.mp4 8MB
  98. 05 - Multivariate Missing Data Imputation/001 Multivariate imputation.mp4 7MB
  99. 10 - Feature Scaling/006 Scaling to minimum and maximum values.mp4 7MB
  100. 03 - Variable Characteristics/009 Variable magnitude.mp4 7MB
  101. 03 - Variable Characteristics/001 Variable characteristics.mp4 7MB
  102. 06 - Categorical Variable Encoding/011 Target guided ordinal encoding.mp4 7MB
  103. 06 - Categorical Variable Encoding/009 Count or frequency encoding.mp4 7MB
  104. 10 - Feature Scaling/010 Scaling to median and quantiles.mp4 7MB
  105. 10 - Feature Scaling/008 Maximum absolute scaling.mp4 7MB
  106. 08 - Discretisation/009 Discretisation plus categorical encoding.mp4 6MB
  107. 01 - Introduction/005 Course material.mp4 6MB
  108. 02 - Variable Types/001 Variables Intro.mp4 5MB
  109. 04 - Missing Data Imputation/021 Frequent category imputation with Feature-engine.mp4 5MB
  110. 06 - Categorical Variable Encoding/013 Mean encoding.mp4 5MB
  111. 06 - Categorical Variable Encoding/007 Ordinal encoding Label encoding.mp4 5MB
  112. 02 - Variable Types/005 Mixed variables.mp4 5MB
  113. 02 - Variable Types/004 Date and time variables.mp4 4MB
  114. 01 - Introduction/009 Moving forward.mp4 4MB
  115. 01 - Introduction/007 Datasets.zip 3MB
  116. 05 - Multivariate Missing Data Imputation/005 missForest.mp4 2MB
  117. 03 - Variable Characteristics/010 ML-Comparison.pdf 298KB
  118. 04 - Missing Data Imputation/026 NA-methods-Comparison.pdf 274KB
  119. 04 - Missing Data Imputation/008 Random sample imputation_en.srt 18KB
  120. 06 - Categorical Variable Encoding/004 One-hot-encoding Demo_en.srt 18KB
  121. 13 - Assembling a feature engineering pipeline/004 Regression pipeline_en.srt 17KB
  122. 06 - Categorical Variable Encoding/017 Weight of Evidence Demo_en.srt 17KB
  123. 13 - Assembling a feature engineering pipeline/003 Classification pipeline_en.srt 17KB
  124. 08 - Discretisation/012 Discretisation with decision trees using Scikit-learn_en.srt 14KB
  125. 06 - Categorical Variable Encoding/018 Comparison of categorical variable encoding_en.srt 13KB
  126. 08 - Discretisation/004 Equal-width discretisation Demo_en.srt 13KB
  127. 06 - Categorical Variable Encoding/020 Rare label encoding Demo_en.srt 12KB
  128. 03 - Variable Characteristics/005 Linear models assumptions_en.srt 11KB
  129. 13 - Assembling a feature engineering pipeline/002 Feature Engineering Pipeline_en.srt 11KB
  130. 03 - Variable Characteristics/008 Outliers_en.srt 11KB
  131. 04 - Missing Data Imputation/003 Mean or median imputation_en.srt 10KB
  132. 06 - Categorical Variable Encoding/006 One hot encoding of top categories Demo_en.srt 10KB
  133. 06 - Categorical Variable Encoding/008 Ordinal encoding Demo_en.srt 10KB
  134. 06 - Categorical Variable Encoding/012 Target guided ordinal encoding Demo_en.srt 10KB
  135. 12 - Engineering datetime variables/002 Engineering dates Demo_en.srt 9KB
  136. 04 - Missing Data Imputation/016 Automatic determination of imputation method with Sklearn_en.srt 9KB
  137. 03 - Variable Characteristics/002 Missing data_en.srt 9KB
  138. 13 - Assembling a feature engineering pipeline/001 Putting it all together_en.srt 9KB
  139. 04 - Missing Data Imputation/004 Arbitrary value imputation_en.srt 9KB
  140. 13 - Assembling a feature engineering pipeline/005 Feature engineering pipeline with cross-validation_en.srt 9KB
  141. 07 - Variable Transformation/002 Variable Transformation with Numpy and SciPy_en.srt 9KB
  142. 04 - Missing Data Imputation/002 Complete Case Analysis_en.srt 9KB
  143. 04 - Missing Data Imputation/006 Frequent category imputation_en.srt 9KB
  144. 05 - Multivariate Missing Data Imputation/003 KNN imputation - Demo_en.srt 9KB
  145. 05 - Multivariate Missing Data Imputation/004 MICE_en.srt 8KB
  146. 04 - Missing Data Imputation/025 CCA with Feature-engine_en.srt 8KB
  147. 09 - Outlier Handling/002 Outlier trimming_en.srt 8KB
  148. 04 - Missing Data Imputation/017 Introduction to Feature-engine_en.srt 8KB
  149. 06 - Categorical Variable Encoding/001 Categorical encoding Introduction_en.srt 8KB
  150. 07 - Variable Transformation/003 Variable Transformation with Scikit-learn_en.srt 8KB
  151. 09 - Outlier Handling/001 Outlier Engineering Intro_en.srt 8KB
  152. 08 - Discretisation/006 Equal-frequency discretisation Demo_en.srt 8KB
  153. 11 - Engineering mixed variables/002 Engineering mixed variables Demo_en.srt 8KB
  154. 06 - Categorical Variable Encoding/021 Binary encoding and feature hashing_en.srt 8KB
  155. 06 - Categorical Variable Encoding/002 One hot encoding_en.srt 7KB
  156. 06 - Categorical Variable Encoding/015 Probability ratio encoding_en.srt 7KB
  157. 09 - Outlier Handling/003 Outlier capping with IQR_en.srt 7KB
  158. 02 - Variable Types/002 Numerical variables_en.srt 7KB
  159. 01 - Introduction/001 Course curriculum overview_en.srt 7KB
  160. 04 - Missing Data Imputation/009 Adding a missing indicator_en.srt 7KB
  161. 10 - Feature Scaling/012 Scaling to vector unit length_en.srt 7KB
  162. 04 - Missing Data Imputation/013 Frequent category imputation with Scikit-learn_en.srt 7KB
  163. 10 - Feature Scaling/002 Standardisation_en.srt 7KB
  164. 06 - Categorical Variable Encoding/014 Mean encoding Demo_en.srt 7KB
  165. 08 - Discretisation/010 Discretisation plus encoding Demo_en.srt 7KB
  166. 10 - Feature Scaling/005 Mean normalisation Demo_en.srt 7KB
  167. 04 - Missing Data Imputation/011 Mean or median imputation with Scikit-learn_en.srt 7KB
  168. 03 - Variable Characteristics/007 Variable distribution_en.srt 6KB
  169. 06 - Categorical Variable Encoding/016 Weight of evidence (WoE)_en.srt 6KB
  170. 04 - Missing Data Imputation/012 Arbitrary value imputation with Scikit-learn_en.srt 6KB
  171. 03 - Variable Characteristics/003 Cardinality - categorical variables_en.srt 6KB
  172. 03 - Variable Characteristics/004 Rare labels - categorical variables_en.srt 6KB
  173. 10 - Feature Scaling/013 Scaling to vector unit length Demo_en.srt 6KB
  174. 04 - Missing Data Imputation/005 End of distribution imputation_en.srt 6KB
  175. 04 - Missing Data Imputation/020 End of distribution imputation with Feature-engine_en.srt 6KB
  176. 08 - Discretisation/011 Discretisation with classification trees_en.srt 6KB
  177. 12 - Engineering datetime variables/003 Engineering time variables and different timezones_en.srt 6KB
  178. 10 - Feature Scaling/003 Standardisation Demo_en.srt 6KB
  179. 07 - Variable Transformation/001 Variable Transformation Introduction_en.srt 6KB
  180. 12 - Engineering datetime variables/001 Engineering datetime variables_en.srt 6KB
  181. 04 - Missing Data Imputation/018 Mean or median imputation with Feature-engine_en.srt 5KB
  182. 06 - Categorical Variable Encoding/010 Count encoding Demo_en.srt 5KB
  183. 04 - Missing Data Imputation/001 Introduction to missing data imputation_en.srt 5KB
  184. 06 - Categorical Variable Encoding/019 Rare label encoding_en.srt 5KB
  185. 05 - Multivariate Missing Data Imputation/006 MICE and missForest - Demo_en.srt 5KB
  186. 09 - Outlier Handling/004 Outlier capping with mean and std_en.srt 5KB
  187. 04 - Missing Data Imputation/010 Imputation with Scikit-learn_en.srt 5KB
  188. 10 - Feature Scaling/004 Mean normalisation_en.srt 5KB
  189. 04 - Missing Data Imputation/007 Missing category imputation_en.srt 5KB
  190. 05 - Multivariate Missing Data Imputation/002 KNN imputation_en.srt 5KB
  191. 08 - Discretisation/005 Equal-frequency discretisation_en.srt 5KB
  192. 04 - Missing Data Imputation/024 Adding a missing indicator with Feature-engine_en.srt 5KB
  193. 10 - Feature Scaling/001 Feature scaling Introduction_en.srt 5KB
  194. 08 - Discretisation/007 K-means discretisation_en.srt 5KB
  195. 04 - Missing Data Imputation/015 Adding a missing indicator with Scikit-learn_en.srt 5KB
  196. 02 - Variable Types/003 Categorical variables_en.srt 5KB
  197. 10 - Feature Scaling/009 MaxAbsScaling Demo_en.srt 5KB
  198. 03 - Variable Characteristics/011 Additional reading resources.html 5KB
  199. 08 - Discretisation/002 Equal-width discretisation_en.srt 5KB
  200. 08 - Discretisation/013 Discretisation with decision trees using Feature-engine_en.srt 4KB
  201. 07 - Variable Transformation/004 Variable transformation with Feature-engine_en.srt 4KB
  202. 08 - Discretisation/014 Domain knowledge discretisation_en.srt 4KB
  203. 03 - Variable Characteristics/009 Variable magnitude_en.srt 4KB
  204. 11 - Engineering mixed variables/001 Engineering mixed variables_en.srt 4KB
  205. 09 - Outlier Handling/006 Arbitrary capping_en.srt 4KB
  206. 05 - Multivariate Missing Data Imputation/001 Multivariate imputation_en.srt 4KB
  207. 10 - Feature Scaling/006 Scaling to minimum and maximum values_en.srt 4KB
  208. 06 - Categorical Variable Encoding/009 Count or frequency encoding_en.srt 4KB
  209. 09 - Outlier Handling/005 Outlier capping with quantiles_en.srt 4KB
  210. 04 - Missing Data Imputation/022 Missing category imputation with Feature-engine_en.srt 4KB
  211. 04 - Missing Data Imputation/019 Arbitrary value imputation with Feature-engine_en.srt 4KB
  212. 06 - Categorical Variable Encoding/005 One hot encoding of top categories_en.srt 4KB
  213. 04 - Missing Data Imputation/014 Missing category imputation with Scikit-learn_en.srt 4KB
  214. 03 - Variable Characteristics/001 Variable characteristics_en.srt 4KB
  215. 10 - Feature Scaling/007 MinMaxScaling Demo_en.srt 4KB
  216. 02 - Variable Types/001 Variables Intro_en.srt 3KB
  217. 01 - Introduction/007 Download datasets.html 3KB
  218. 01 - Introduction/002 Course requirements_en.srt 3KB
  219. 08 - Discretisation/001 Discretisation Introduction_en.srt 3KB
  220. 06 - Categorical Variable Encoding/011 Target guided ordinal encoding_en.srt 3KB
  221. 10 - Feature Scaling/008 Maximum absolute scaling_en.srt 3KB
  222. 10 - Feature Scaling/010 Scaling to median and quantiles_en.srt 3KB
  223. 08 - Discretisation/008 K-means discretisation Demo_en.srt 3KB
  224. 01 - Introduction/004 Setting up your computer.html 3KB
  225. 08 - Discretisation/009 Discretisation plus categorical encoding_en.srt 3KB
  226. 06 - Categorical Variable Encoding/013 Mean encoding_en.srt 3KB
  227. 04 - Missing Data Imputation/023 Random sample imputation with Feature-engine_en.srt 3KB
  228. 02 - Variable Types/005 Mixed variables_en.srt 3KB
  229. 04 - Missing Data Imputation/027 Conclusion when to use each missing data imputation method.html 3KB
  230. 01 - Introduction/009 Moving forward_en.srt 2KB
  231. 02 - Variable Types/004 Date and time variables_en.srt 2KB
  232. 10 - Feature Scaling/011 Robust Scaling Demo_en.srt 2KB
  233. 06 - Categorical Variable Encoding/023 Additional reading resources.html 2KB
  234. 01 - Introduction/005 Course material_en.srt 2KB
  235. 06 - Categorical Variable Encoding/007 Ordinal encoding Label encoding_en.srt 2KB
  236. 04 - Missing Data Imputation/021 Frequent category imputation with Feature-engine_en.srt 2KB
  237. 01 - Introduction/010 FAQ Data science, Python, datasets, presentations and more.html 2KB
  238. 01 - Introduction/003 How to approach this course.html 2KB
  239. 03 - Variable Characteristics/006 Linear model assumptions - additional reading resources (optional).html 1KB
  240. 08 - Discretisation/015 Additional reading resources.html 1KB
  241. 10 - Feature Scaling/014 Additional reading resources.html 1KB
  242. 05 - Multivariate Missing Data Imputation/005 missForest_en.srt 1KB
  243. 05 - Multivariate Missing Data Imputation/007 Additional reading resources (Optional).html 1KB
  244. 01 - Introduction/006 Download Jupyter notebooks.html 1019B
  245. 06 - Categorical Variable Encoding/003 Important Feature-engine version 1.0.0.html 1009B
  246. 14 - Final section Next steps/001 Survey.html 947B
  247. 08 - Discretisation/003 Important Feature-engine v 1.0.0.html 739B
  248. 14 - Final section Next steps/003 Bonus lecture.html 625B
  249. 14 - Final section Next steps/002 Congratulations.html 593B
  250. 09 - Outlier Handling/008 Additional reading resources.html 526B
  251. 03 - Variable Characteristics/010 Variable characteristics and machine learning models.html 402B
  252. 04 - Missing Data Imputation/026 Overview of missing value imputation methods.html 339B
  253. 06 - Categorical Variable Encoding/022 Summary table of encoding techniques.html 312B
  254. 13 - Assembling a feature engineering pipeline/006 More examples.html 308B
  255. 01 - Introduction/008 Download presentations.html 286B
  256. 09 - Outlier Handling/007 Important Feature-engine v1.0.0.html 262B
  257. 0. Websites you may like/[Tutorialsplanet.NET].url 128B
  258. 03 - Variable Characteristics/[Tutorialsplanet.NET].url 128B
  259. 05 - Multivariate Missing Data Imputation/[Tutorialsplanet.NET].url 128B
  260. 07 - Variable Transformation/[Tutorialsplanet.NET].url 128B
  261. 11 - Engineering mixed variables/[Tutorialsplanet.NET].url 128B
  262. 13 - Assembling a feature engineering pipeline/[Tutorialsplanet.NET].url 128B
  263. [Tutorialsplanet.NET].url 128B