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[] Udemy - Data Science Supervised Machine Learning in Python

  • 收录时间:2019-02-21 20:06:02
  • 文件大小:409MB
  • 下载次数:145
  • 最近下载:2021-01-23 16:54:51
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文件列表

  1. 09 Appendix/035 How to install Numpy Scipy Matplotlib and Sci-Kit Learn.mp4 44MB
  2. 04 Decision Trees/021 Decision Tree in Code.mp4 30MB
  3. 09 Appendix/036 How to Code by Yourself part 1.mp4 25MB
  4. 02 K-Nearest Neighbor/006 KNN in Code with MNIST.mp4 18MB
  5. 06 Practical Machine Learning/030 Sci-Kit Learn.mp4 16MB
  6. 03 Naive Bayes and Bayes Classifiers/010 Naive Bayes.mp4 16MB
  7. 09 Appendix/037 How to Code by Yourself part 2.mp4 15MB
  8. 03 Naive Bayes and Bayes Classifiers/012 Naive Bayes in Code with MNIST.mp4 14MB
  9. 04 Decision Trees/019 Maximizing Information Gain.mp4 14MB
  10. 05 Perceptrons/023 Perceptron in Code.mp4 14MB
  11. 05 Perceptrons/022 Perceptron Concepts.mp4 12MB
  12. 07 Building a Machine Learning Web Service/033 Building a Machine Learning Web Service Code.mp4 12MB
  13. 06 Practical Machine Learning/031 Regression with Sci-Kit Learn is Easy.mp4 11MB
  14. 03 Naive Bayes and Bayes Classifiers/015 Linear Discriminant Analysis LDA and Quadratic Discriminant Analysis QDA.mp4 10MB
  15. 01 Introduction and Review/004 How to Succeed in this Course.mp4 9MB
  16. 05 Perceptrons/024 Perceptron for MNIST and XOR.mp4 9MB
  17. 06 Practical Machine Learning/028 Comparison to Deep Learning.mp4 9MB
  18. 02 K-Nearest Neighbor/005 K-Nearest Neighbor Concepts.mp4 9MB
  19. 04 Decision Trees/017 Decision Tree Basics.mp4 8MB
  20. 02 K-Nearest Neighbor/007 When KNN Can Fail.mp4 8MB
  21. 01 Introduction and Review/001 Introduction and Outline.mp4 8MB
  22. 06 Practical Machine Learning/026 Hyperparameters and Cross-Validation.mp4 7MB
  23. 03 Naive Bayes and Bayes Classifiers/013 Non-Naive Bayes.mp4 7MB
  24. 07 Building a Machine Learning Web Service/032 Building a Machine Learning Web Service Concepts.mp4 7MB
  25. 06 Practical Machine Learning/027 Feature Extraction and Feature Selection.mp4 7MB
  26. 04 Decision Trees/018 Information Entropy.mp4 7MB
  27. 05 Perceptrons/025 Perceptron Loss Function.mp4 7MB
  28. 04 Decision Trees/020 Choosing the Best Split.mp4 7MB
  29. 08 Conclusion/034 Whats Next Support Vector Machines and Ensemble Methods e.g. Random Forest.mp4 6MB
  30. 01 Introduction and Review/002 Review of Important Concepts.mp4 6MB
  31. 03 Naive Bayes and Bayes Classifiers/011 Naive Bayes Handwritten Example.mp4 6MB
  32. 06 Practical Machine Learning/029 Multiclass Classification.mp4 6MB
  33. 02 K-Nearest Neighbor/009 KNN for the Donut Problem.mp4 5MB
  34. 03 Naive Bayes and Bayes Classifiers/016 Generative vs Discriminative Models.mp4 5MB
  35. 03 Naive Bayes and Bayes Classifiers/014 Bayes Classifier in Code with MNIST.mp4 4MB
  36. 02 K-Nearest Neighbor/008 KNN for the XOR Problem.mp4 4MB
  37. 09 Appendix/038 Where to get Udemy coupons and FREE deep learning material.mp4 4MB
  38. 01 Introduction and Review/003 Where to get the Code and Data.mp4 4MB
  39. [DesireCourse.Com].txt 754B
  40. [DesireCourse.Com].url 51B