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

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 2019-3-16 11:17 2024-12-30 15:00 195 1.98 GB 64
磁力链接
magnet:?xt=urn:btih:c45815dc20aa1d329b65df761df7edf3e911294c
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOmM0NTgxNWRjMjBhYTFkMzI5YjY1ZGY3NjFkZjdlZGYzZTkxMTI5NGNaWg==
二维码链接
[Coursezone.net] Coursera - How to Win a Data Science Competition Learn from Top Kagglers的二维码
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Coursezone net Coursera - How to Win a Data Science Competition Learn from Top Kagglers

文件列表
  1. 001.Welcome to How to win a data science competition/001. Introduction.mp49.72MB
  2. 001.Welcome to How to win a data science competition/002. Meet your lecturers.mp413.85MB
  3. 001.Welcome to How to win a data science competition/003. Course overview.mp434.63MB
  4. 002.Competition mechanics/004. Competition Mechanics.mp424.94MB
  5. 002.Competition mechanics/005. Kaggle Overview [screencast].mp432.38MB
  6. 002.Competition mechanics/006. Real World Application vs Competitions.mp420MB
  7. 003.Recap of main ML algorithms/007. Recap of main ML algorithms.mp433.45MB
  8. 004.Software Hardware requirements/008. Software Hardware Requirements.mp421.53MB
  9. 005.Feature preprocessing and generation with respect to models/009. Overview.mp425.67MB
  10. 005.Feature preprocessing and generation with respect to models/010. Numeric features.mp448.31MB
  11. 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp440.55MB
  12. 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.mp432.41MB
  13. 005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp437.85MB
  14. 006.Feature extraction from text and images/014. Bag of words.mp438.04MB
  15. 006.Feature extraction from text and images/015. Word2vec, CNN.mp445.96MB
  16. 007.Final project/016. Final project overview.mp417.81MB
  17. 008.Exploratory data analysis/017. Exploratory data analysis.mp423.95MB
  18. 008.Exploratory data analysis/018. Building intuition about the data.mp422.29MB
  19. 008.Exploratory data analysis/019. Exploring anonymized data.mp443.05MB
  20. 008.Exploratory data analysis/020. Visualizations.mp442.59MB
  21. 008.Exploratory data analysis/021. Dataset cleaning and other things to check.mp425.83MB
  22. 009.EDA examples/022. Springleaf competition EDA I.mp420.11MB
  23. 009.EDA examples/023. Springleaf competition EDA II.mp444.36MB
  24. 009.EDA examples/024. Numerai competition EDA.mp421.95MB
  25. 010.Validation/025. Validation and overfitting.mp434.09MB
  26. 010.Validation/026. Validation strategies.mp426.15MB
  27. 010.Validation/027. Data splitting strategies.mp456.16MB
  28. 010.Validation/028. Problems occurring during validation.mp426.53MB
  29. 011.Data leakages/029. Basic data leaks.mp422.14MB
  30. 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.mp434.07MB
  31. 011.Data leakages/031. Expedia challenge.mp435.65MB
  32. 012.Metrics optimization/032. Motivation.mp427.49MB
  33. 012.Metrics optimization/033. Regression metrics review I.mp446.38MB
  34. 012.Metrics optimization/034. Regression metrics review II.mp429.2MB
  35. 012.Metrics optimization/035. Classification metrics review.mp470.29MB
  36. 012.Metrics optimization/036. General approaches for metrics optimization.mp423.71MB
  37. 012.Metrics optimization/037. Regression metrics optimization.mp435.84MB
  38. 012.Metrics optimization/038. Classification metrics optimization I.mp426.25MB
  39. 012.Metrics optimization/039. Classification metrics optimization II.mp425.24MB
  40. 013.Mean encodings/040. Concept of mean encoding.mp430.55MB
  41. 013.Mean encodings/041. Regularization.mp428.37MB
  42. 013.Mean encodings/042. Extensions and generalizations.mp439.24MB
  43. 014.Hyperparameter tuning/043. Hyperparameter tuning I.mp424.96MB
  44. 014.Hyperparameter tuning/044. Hyperparameter tuning II.mp443.3MB
  45. 014.Hyperparameter tuning/045. Hyperparameter tuning III.mp447.18MB
  46. 015.Tips and tricks/046. Practical guide.mp459.12MB
  47. 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.mp433.81MB
  48. 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.mp432MB
  49. 016.Advanced features II/049. Statistics and distance based features.mp420.96MB
  50. 016.Advanced features II/050. Matrix factorizations.mp424.14MB
  51. 016.Advanced features II/051. Feature Interactions.mp420.41MB
  52. 016.Advanced features II/052. t-SNE.mp421.56MB
  53. 017.Ensembling/053. Introduction into ensemble methods.mp410.68MB
  54. 017.Ensembling/054. Bagging.mp415.93MB
  55. 017.Ensembling/055. Boosting.mp427.94MB
  56. 017.Ensembling/056. Stacking.mp430.78MB
  57. 017.Ensembling/057. StackNet.mp429.25MB
  58. 017.Ensembling/058. Ensembling Tips and Tricks.mp425.59MB
  59. 018.Competitions go through/059. Crowdflower Competition.mp436.12MB
  60. 018.Competitions go through/060. Springleaf Marketing Response.mp424.24MB
  61. 018.Competitions go through/061. Microsoft Malware Classification Challenge.mp468.35MB
  62. 018.Competitions go through/062. Walmart Trip Type Classification.mp429.55MB
  63. 018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.mp434.77MB
  64. 018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.mp430.87MB
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