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[] Coursera - How to Win a Data Science Competition Learn from Top Kagglers

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文件列表

  1. 010.Validation/028. Problems occurring during validation.mp4 71MB
  2. 012.Metrics optimization/035. Classification metrics review.mp4 70MB
  3. 018.Competitions go through/061. Microsoft Malware Classification Challenge.mp4 68MB
  4. 015.Tips and tricks/046. Practical guide.mp4 59MB
  5. 010.Validation/027. Data splitting strategies.mp4 56MB
  6. 005.Feature preprocessing and generation with respect to models/010. Numeric features.mp4 48MB
  7. 014.Hyperparameter tuning/045. Hyperparameter tuning III.mp4 47MB
  8. 012.Metrics optimization/033. Regression metrics review I.mp4 46MB
  9. 006.Feature extraction from text and images/015. Word2vec, CNN.mp4 46MB
  10. 009.EDA examples/023. Springleaf competition EDA II.mp4 44MB
  11. 014.Hyperparameter tuning/044. Hyperparameter tuning II.mp4 43MB
  12. 008.Exploratory data analysis/019. Exploring anonymized data.mp4 43MB
  13. 008.Exploratory data analysis/020. Visualizations.mp4 43MB
  14. 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp4 41MB
  15. 013.Mean encodings/042. Extensions and generalizations.mp4 39MB
  16. 006.Feature extraction from text and images/014. Bag of words.mp4 38MB
  17. 005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp4 38MB
  18. 018.Competitions go through/059. Crowdflower Competition.mp4 36MB
  19. 012.Metrics optimization/037. Regression metrics optimization.mp4 36MB
  20. 011.Data leakages/031. Expedia challenge.mp4 36MB
  21. 018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.mp4 35MB
  22. 001.Welcome to How to win a data science competition/003. Course overview.mp4 35MB
  23. 010.Validation/025. Validation and overfitting.mp4 34MB
  24. 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.mp4 34MB
  25. 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.mp4 34MB
  26. 003.Recap of main ML algorithms/007. Recap of main ML algorithms.mp4 33MB
  27. 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.mp4 32MB
  28. 002.Competition mechanics/005. Kaggle Overview [screencast].mp4 32MB
  29. 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.mp4 32MB
  30. 018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.mp4 31MB
  31. 017.Ensembling/056. Stacking.mp4 31MB
  32. 013.Mean encodings/040. Concept of mean encoding.mp4 31MB
  33. 018.Competitions go through/062. Walmart Trip Type Classification.mp4 30MB
  34. 017.Ensembling/057. StackNet.mp4 29MB
  35. 012.Metrics optimization/034. Regression metrics review II.mp4 29MB
  36. 013.Mean encodings/041. Regularization.mp4 28MB
  37. 017.Ensembling/055. Boosting.mp4 28MB
  38. 012.Metrics optimization/032. Motivation.mp4 27MB
  39. 012.Metrics optimization/038. Classification metrics optimization I.mp4 26MB
  40. 010.Validation/026. Validation strategies.mp4 26MB
  41. 008.Exploratory data analysis/021. Dataset cleaning and other things to check.mp4 26MB
  42. 005.Feature preprocessing and generation with respect to models/009. Overview.mp4 26MB
  43. 017.Ensembling/058. Ensembling Tips and Tricks.mp4 26MB
  44. 012.Metrics optimization/039. Classification metrics optimization II.mp4 25MB
  45. 014.Hyperparameter tuning/043. Hyperparameter tuning I.mp4 25MB
  46. 002.Competition mechanics/004. Competition Mechanics.mp4 25MB
  47. 018.Competitions go through/060. Springleaf Marketing Response.mp4 24MB
  48. 016.Advanced features II/050. Matrix factorizations.mp4 24MB
  49. 008.Exploratory data analysis/017. Exploratory data analysis.mp4 24MB
  50. 012.Metrics optimization/036. General approaches for metrics optimization.mp4 24MB
  51. 008.Exploratory data analysis/018. Building intuition about the data.mp4 22MB
  52. 011.Data leakages/029. Basic data leaks.mp4 22MB
  53. 009.EDA examples/024. Numerai competition EDA.mp4 22MB
  54. 016.Advanced features II/052. t-SNE.mp4 22MB
  55. 004.Software Hardware requirements/008. Software Hardware Requirements.mp4 22MB
  56. 016.Advanced features II/049. Statistics and distance based features.mp4 21MB
  57. 016.Advanced features II/051. Feature Interactions.mp4 20MB
  58. 009.EDA examples/022. Springleaf competition EDA I.mp4 20MB
  59. 002.Competition mechanics/006. Real World Application vs Competitions.mp4 20MB
  60. 007.Final project/016. Final project overview.mp4 18MB
  61. 017.Ensembling/054. Bagging.mp4 16MB
  62. 001.Welcome to How to win a data science competition/002. Meet your lecturers.mp4 14MB
  63. 017.Ensembling/053. Introduction into ensemble methods.mp4 11MB
  64. 001.Welcome to How to win a data science competition/001. Introduction.mp4 10MB
  65. 010.Validation/028. Problems occurring during validation.srt 25KB
  66. 018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.srt 25KB
  67. 012.Metrics optimization/035. Classification metrics review.srt 24KB
  68. 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.srt 23KB
  69. 018.Competitions go through/061. Microsoft Malware Classification Challenge.srt 23KB
  70. 015.Tips and tricks/046. Practical guide.srt 22KB
  71. 018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.srt 22KB
  72. 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.srt 22KB
  73. 009.EDA examples/023. Springleaf competition EDA II.srt 20KB
  74. 017.Ensembling/055. Boosting.srt 19KB
  75. 017.Ensembling/056. Stacking.srt 19KB
  76. 010.Validation/027. Data splitting strategies.srt 19KB
  77. 005.Feature preprocessing and generation with respect to models/010. Numeric features.srt 19KB
  78. 008.Exploratory data analysis/019. Exploring anonymized data.srt 18KB
  79. 017.Ensembling/057. StackNet.srt 18KB
  80. 017.Ensembling/058. Ensembling Tips and Tricks.srt 18KB
  81. 012.Metrics optimization/033. Regression metrics review I.srt 17KB
  82. 006.Feature extraction from text and images/015. Word2vec, CNN.srt 17KB
  83. 008.Exploratory data analysis/020. Visualizations.srt 16KB
  84. 018.Competitions go through/059. Crowdflower Competition.srt 15KB
  85. 014.Hyperparameter tuning/045. Hyperparameter tuning III.srt 15KB
  86. 014.Hyperparameter tuning/044. Hyperparameter tuning II.srt 15KB
  87. 006.Feature extraction from text and images/014. Bag of words.srt 14KB
  88. 003.Recap of main ML algorithms/007. Recap of main ML algorithms.srt 14KB
  89. 010.Validation/025. Validation and overfitting.srt 13KB
  90. 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.srt 13KB
  91. 005.Feature preprocessing and generation with respect to models/013. Handling missing values.srt 13KB
  92. 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.srt 12KB
  93. 013.Mean encodings/042. Extensions and generalizations.srt 12KB
  94. 012.Metrics optimization/037. Regression metrics optimization.srt 12KB
  95. 011.Data leakages/031. Expedia challenge.srt 11KB
  96. 017.Ensembling/054. Bagging.srt 11KB
  97. 002.Competition mechanics/004. Competition Mechanics.srt 11KB
  98. 012.Metrics optimization/032. Motivation.srt 11KB
  99. 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.srt 10KB
  100. 001.Welcome to How to win a data science competition/003. Course overview.srt 10KB
  101. 018.Competitions go through/062. Walmart Trip Type Classification.srt 10KB
  102. 013.Mean encodings/040. Concept of mean encoding.srt 10KB
  103. 008.Exploratory data analysis/017. Exploratory data analysis.srt 10KB
  104. 008.Exploratory data analysis/021. Dataset cleaning and other things to check.srt 10KB
  105. 012.Metrics optimization/034. Regression metrics review II.srt 10KB
  106. 008.Exploratory data analysis/018. Building intuition about the data.srt 9KB
  107. 002.Competition mechanics/005. Kaggle Overview [screencast].srt 9KB
  108. 013.Mean encodings/041. Regularization.srt 9KB
  109. 010.Validation/026. Validation strategies.srt 9KB
  110. 016.Advanced features II/050. Matrix factorizations.srt 9KB
  111. 009.EDA examples/022. Springleaf competition EDA I.srt 9KB
  112. 005.Feature preprocessing and generation with respect to models/009. Overview.srt 9KB
  113. 012.Metrics optimization/038. Classification metrics optimization I.srt 9KB
  114. 014.Hyperparameter tuning/043. Hyperparameter tuning I.srt 9KB
  115. 012.Metrics optimization/039. Classification metrics optimization II.srt 9KB
  116. 002.Competition mechanics/006. Real World Application vs Competitions.srt 9KB
  117. 011.Data leakages/029. Basic data leaks.srt 8KB
  118. 012.Metrics optimization/036. General approaches for metrics optimization.srt 8KB
  119. 004.Software Hardware requirements/008. Software Hardware Requirements.srt 8KB
  120. 018.Competitions go through/060. Springleaf Marketing Response.srt 8KB
  121. 016.Advanced features II/051. Feature Interactions.srt 8KB
  122. 009.EDA examples/024. Numerai competition EDA.srt 8KB
  123. 016.Advanced features II/052. t-SNE.srt 7KB
  124. 017.Ensembling/053. Introduction into ensemble methods.srt 7KB
  125. 016.Advanced features II/049. Statistics and distance based features.srt 7KB
  126. 007.Final project/016. Final project overview.srt 5KB
  127. 001.Welcome to How to win a data science competition/002. Meet your lecturers.srt 4KB
  128. 001.Welcome to How to win a data science competition/001. Introduction.srt 3KB
  129. [FCS Forum].url 133B
  130. [FreeCourseSite.com].url 127B
  131. [CourseClub.NET].url 123B