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  1. Урок 1. 00:06:05
    Introduction
  2. Урок 2. 00:06:37
    Prerequisites
  3. Урок 3. 00:21:16
    Naive Bayes
  4. Урок 4. 00:23:05
    Performance
  5. Урок 5. 00:29:17
    Naive Bayes Optimizations
  6. Урок 6. 00:16:51
    K Nearest Neighbors
  7. Урок 7. 00:21:30
    Decision Trees
  8. Урок 8. 00:19:15
    Linear Regression
  9. Урок 9. 00:32:31
    Logistic Regression
  10. Урок 10. 00:24:04
    Support Vector Machine
  11. Урок 11. 00:16:42
    K-means
  12. Урок 12. 00:18:14
    Singular Value Decomposition
  13. Урок 13. 00:37:18
    Neural Networks
  14. Урок 14. 00:35:31
    Convolutional Neural Networks
  15. Урок 15. 00:28:09
    Recurrent Neural Networks
  16. Урок 16. 00:15:43
    Generative Adversarial Neural Networks
  17. Урок 17. 00:20:18
    Collaborative and Content Based Filtering
  18. Урок 18. 00:22:48
    Learning To Rank