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[] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

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  • 文件大小:881MB
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文件列表

  1. 5. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 78MB
  3. 5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  4. 5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  5. 5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  6. 5. Appendix/11. What order should I take your courses in (part 2).mp4 38MB
  7. 3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.mp4 35MB
  8. 2. K-Means Clustering/5. Soft K-Means in Python Code.mp4 30MB
  9. 4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.mp4 30MB
  10. 5. Appendix/10. What order should I take your courses in (part 1).mp4 29MB
  11. 3. Hierarchical Clustering/4. Application Evolution.mp4 26MB
  12. 2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.mp4 26MB
  13. 2. K-Means Clustering/3. Soft K-Means.mp4 25MB
  14. 5. Appendix/4. How to Code by Yourself (part 1).mp4 25MB
  15. 5. Appendix/6. How to Succeed in this Course (Long Version).mp4 18MB
  16. 2. K-Means Clustering/7. Examples of where K-Means can fail.mp4 17MB
  17. 5. Appendix/5. How to Code by Yourself (part 2).mp4 15MB
  18. 2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.mp4 13MB
  19. 3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 12MB
  20. 2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 11MB
  21. 2. K-Means Clustering/10. Using K-Means on Real Data MNIST.mp4 11MB
  22. 2. K-Means Clustering/11. One Way to Choose K.mp4 9MB
  23. 5. Appendix/9. Python 2 vs Python 3.mp4 8MB
  24. 1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.mp4 8MB
  25. 1. Introduction to Unsupervised Learning/3. Why Use Clustering.mp4 7MB
  26. 3. Hierarchical Clustering/2. Agglomerative Clustering Options.mp4 6MB
  27. 5. Appendix/1. What is the Appendix.mp4 5MB
  28. 2. K-Means Clustering/6. Visualizing Each Step of K-Means.mp4 5MB
  29. 4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.mp4 5MB
  30. 4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.mp4 5MB
  31. 2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.mp4 5MB
  32. 3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 4MB
  33. 1. Introduction to Unsupervised Learning/1. Introduction and Outline.mp4 4MB
  34. 2. K-Means Clustering/8. Disadvantages of K-Means Clustering.mp4 4MB
  35. 4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.mp4 4MB
  36. 4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.mp4 4MB
  37. 1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.mp4 3MB
  38. 2. K-Means Clustering/4. The K-Means Objective Function.mp4 3MB
  39. 4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.mp4 3MB
  40. 4. Gaussian Mixture Models (GMMs)/7. Future Unsupervised Learning Algorithms You Will Learn.mp4 2MB
  41. 5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  42. 5. Appendix/11. What order should I take your courses in (part 2).vtt 20KB
  43. 5. Appendix/4. How to Code by Yourself (part 1).vtt 20KB
  44. 5. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17KB
  45. 3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.vtt 17KB
  46. 3. Hierarchical Clustering/4. Application Evolution.vtt 14KB
  47. 5. Appendix/10. What order should I take your courses in (part 1).vtt 14KB
  48. 5. Appendix/6. How to Succeed in this Course (Long Version).vtt 13KB
  49. 5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12KB
  50. 5. Appendix/5. How to Code by Yourself (part 2).vtt 12KB
  51. 2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.vtt 8KB
  52. 2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).vtt 8KB
  53. 2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.vtt 7KB
  54. 2. K-Means Clustering/5. Soft K-Means in Python Code.vtt 7KB
  55. 4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.vtt 7KB
  56. 2. K-Means Clustering/10. Using K-Means on Real Data MNIST.vtt 6KB
  57. 2. K-Means Clustering/3. Soft K-Means.vtt 6KB
  58. 5. Appendix/9. Python 2 vs Python 3.vtt 5KB
  59. 1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.vtt 5KB
  60. 1. Introduction to Unsupervised Learning/3. Why Use Clustering.vtt 5KB
  61. 3. Hierarchical Clustering/2. Agglomerative Clustering Options.vtt 5KB
  62. 2. K-Means Clustering/7. Examples of where K-Means can fail.vtt 4KB
  63. 2. K-Means Clustering/11. One Way to Choose K.vtt 4KB
  64. 3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.vtt 4KB
  65. 4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.vtt 4KB
  66. 1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.vtt 3KB
  67. 4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.vtt 3KB
  68. 2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.vtt 3KB
  69. 5. Appendix/1. What is the Appendix.vtt 3KB
  70. 1. Introduction to Unsupervised Learning/1. Introduction and Outline.vtt 3KB
  71. 3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.vtt 3KB
  72. 2. K-Means Clustering/8. Disadvantages of K-Means Clustering.vtt 3KB
  73. 4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.vtt 3KB
  74. 4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.vtt 2KB
  75. 2. K-Means Clustering/6. Visualizing Each Step of K-Means.vtt 2KB
  76. 4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.vtt 2KB
  77. 2. K-Means Clustering/4. The K-Means Objective Function.vtt 2KB
  78. 4. Gaussian Mixture Models (GMMs)/7. Future Unsupervised Learning Algorithms You Will Learn.vtt 1KB
  79. [FCS Forum].url 133B
  80. [FreeCourseSite.com].url 127B