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[] Coursera - Applied Machine Learning in Python

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  • 文件大小:881MB
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  • 最近下载:2020-12-19 17:36:16
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文件列表

  1. 003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 46MB
  2. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 45MB
  3. 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 42MB
  4. 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 40MB
  5. 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 39MB
  6. 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 38MB
  7. 002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 38MB
  8. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 36MB
  9. 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 34MB
  10. 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 33MB
  11. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 32MB
  12. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 32MB
  13. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 31MB
  14. 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 30MB
  15. 005.Optional Unsupervised Machine Learning/034. Clustering.mp4 27MB
  16. 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 26MB
  17. 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 23MB
  18. 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 23MB
  19. 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 21MB
  20. 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 21MB
  21. 002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20MB
  22. 002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 20MB
  23. 003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 20MB
  24. 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 20MB
  25. 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 17MB
  26. 003.Module 3 Evaluation/024. Regression Evaluation.mp4 17MB
  27. 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16MB
  28. 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 15MB
  29. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 13MB
  30. 003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 13MB
  31. 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 12MB
  32. 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11MB
  33. 005.Optional Unsupervised Machine Learning/032. Introduction.mp4 11MB
  34. 006.Conclusion/035. Conclusion.mp4 10MB
  35. 003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9MB
  36. 003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 30KB
  37. 002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 28KB
  38. 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 28KB
  39. 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 27KB
  40. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 26KB
  41. 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 26KB
  42. 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 22KB
  43. 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 21KB
  44. 005.Optional Unsupervised Machine Learning/034. Clustering.srt 20KB
  45. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 19KB
  46. 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 18KB
  47. 002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 17KB
  48. 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 17KB
  49. 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 17KB
  50. 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 17KB
  51. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt 16KB
  52. 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt 16KB
  53. 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt 16KB
  54. 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt 16KB
  55. 003.Module 3 Evaluation/023. Multi-Class Evaluation.srt 15KB
  56. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt 15KB
  57. 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt 13KB
  58. 002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt 13KB
  59. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt 12KB
  60. 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt 11KB
  61. 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt 10KB
  62. 003.Module 3 Evaluation/021. Classifier Decision Functions.srt 9KB
  63. 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt 8KB
  64. 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt 8KB
  65. 003.Module 3 Evaluation/024. Regression Evaluation.srt 8KB
  66. 003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt 8KB
  67. 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt 7KB
  68. 005.Optional Unsupervised Machine Learning/032. Introduction.srt 6KB
  69. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt 6KB
  70. 006.Conclusion/035. Conclusion.srt 4KB
  71. [CourseClub.NET].url 123B
  72. [FreeCourseSite.Com].url 53B
  73. [DesireCourse.Com].url 51B