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[] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost

  • 收录时间:2020-01-17 22:56:21
  • 文件大小:826MB
  • 下载次数:48
  • 最近下载:2020-12-15 03:28:04
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文件列表

  1. 6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  3. 3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp4 48MB
  4. 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  5. 2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4 42MB
  6. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  7. 6. Appendix/13. What order should I take your courses in (part 2).mp4 38MB
  8. 6. Appendix/12. What order should I take your courses in (part 1).mp4 29MB
  9. 6. Appendix/5. How to Code by Yourself (part 1).mp4 25MB
  10. 3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp4 20MB
  11. 3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp4 16MB
  12. 5. AdaBoost/4. AdaBoost Implementation.mp4 16MB
  13. 4. Random Forest/2. Random Forest Regressor.mp4 15MB
  14. 6. Appendix/6. How to Code by Yourself (part 2).mp4 15MB
  15. 4. Random Forest/1. Random Forest Algorithm.mp4 14MB
  16. 2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp4 14MB
  17. 6. Appendix/7. How to Succeed in this Course (Long Version).mp4 13MB
  18. 6. Appendix/2. Confidence Intervals.mp4 13MB
  19. 4. Random Forest/3. Random Forest Classifier.mp4 13MB
  20. 5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp4 11MB
  21. 3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.mp4 11MB
  22. 5. AdaBoost/1. AdaBoost Algorithm.mp4 11MB
  23. 2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.mp4 10MB
  24. 4. Random Forest/5. Implementing a Not as Random Forest.mp4 9MB
  25. 6. Appendix/11. Python 2 vs Python 3.mp4 8MB
  26. 4. Random Forest/4. Random Forest vs Bagging Trees.mp4 8MB
  27. 5. AdaBoost/7. Summary and What's Next.mp4 7MB
  28. 1. Get Started/1. Outline and Motivation.mp4 7MB
  29. 2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.mp4 7MB
  30. 3. Bootstrap Estimates and Bagging/6. Stacking.mp4 6MB
  31. 5. AdaBoost/6. Connection to Deep Learning.mp4 6MB
  32. 6. Appendix/1. What is the Appendix.mp4 5MB
  33. 5. AdaBoost/5. Comparison to Stacking.mp4 5MB
  34. 2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp4 5MB
  35. 1. Get Started/3. All Data is the Same.mp4 5MB
  36. 2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.mp4 5MB
  37. 4. Random Forest/6. Connection to Deep Learning Dropout.mp4 4MB
  38. 6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4MB
  39. 3. Bootstrap Estimates and Bagging/3. Bagging.mp4 4MB
  40. 1. Get Started/4. Plug-and-Play.mp4 4MB
  41. 1. Get Started/2. Where to get the Code and Data.mp4 3MB
  42. 5. AdaBoost/2. Additive Modeling.mp4 3MB
  43. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  44. 6. Appendix/13. What order should I take your courses in (part 2).vtt 20KB
  45. 6. Appendix/5. How to Code by Yourself (part 1).vtt 20KB
  46. 6. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17KB
  47. 6. Appendix/12. What order should I take your courses in (part 1).vtt 14KB
  48. 6. Appendix/7. How to Succeed in this Course (Long Version).vtt 13KB
  49. 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12KB
  50. 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12KB
  51. 6. Appendix/6. How to Code by Yourself (part 2).vtt 12KB
  52. 6. Appendix/2. Confidence Intervals.vtt 12KB
  53. 2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.vtt 11KB
  54. 3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.vtt 11KB
  55. 4. Random Forest/1. Random Forest Algorithm.vtt 11KB
  56. 5. AdaBoost/4. AdaBoost Implementation.vtt 10KB
  57. 5. AdaBoost/1. AdaBoost Algorithm.vtt 8KB
  58. 2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.vtt 8KB
  59. 4. Random Forest/2. Random Forest Regressor.vtt 7KB
  60. 5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.vtt 7KB
  61. 1. Get Started/1. Outline and Motivation.vtt 6KB
  62. 5. AdaBoost/7. Summary and What's Next.vtt 5KB
  63. 6. Appendix/11. Python 2 vs Python 3.vtt 5KB
  64. 2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.vtt 5KB
  65. 2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.vtt 5KB
  66. 4. Random Forest/3. Random Forest Classifier.vtt 5KB
  67. 3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.vtt 5KB
  68. 3. Bootstrap Estimates and Bagging/6. Stacking.vtt 4KB
  69. 4. Random Forest/5. Implementing a Not as Random Forest.vtt 4KB
  70. 5. AdaBoost/6. Connection to Deep Learning.vtt 4KB
  71. 3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.vtt 4KB
  72. 1. Get Started/3. All Data is the Same.vtt 4KB
  73. 4. Random Forest/4. Random Forest vs Bagging Trees.vtt 4KB
  74. 5. AdaBoost/5. Comparison to Stacking.vtt 4KB
  75. 3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.vtt 4KB
  76. 2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.vtt 4KB
  77. 2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.vtt 4KB
  78. 6. Appendix/1. What is the Appendix.vtt 3KB
  79. 6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3KB
  80. 4. Random Forest/6. Connection to Deep Learning Dropout.vtt 3KB
  81. 3. Bootstrap Estimates and Bagging/3. Bagging.vtt 3KB
  82. 1. Get Started/4. Plug-and-Play.vtt 3KB
  83. 1. Get Started/2. Where to get the Code and Data.vtt 3KB
  84. 5. AdaBoost/2. Additive Modeling.vtt 2KB
  85. [DesireCourse.Com].url 51B