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Coursera - Applied Machine Learning in Python

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  • 文件大小:553MB
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文件列表

  1. 03_module-3/01_module-3-evaluation/01_model-evaluation-selection.mp4 32MB
  2. 02_module-2/01_module-2-supervised-machine-learning/05_linear-regression-least-squares.mp4 30MB
  3. 02_module-2/01_module-2-supervised-machine-learning/06_linear-regression-ridge-lasso-and-polynomial-regression.mp4 29MB
  4. 02_module-2/01_module-2-supervised-machine-learning/12_decision-trees.mp4 27MB
  5. 04_module-4/01_module-4-supervised-machine-learning-part-2/05_neural-networks.mp4 27MB
  6. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/07_k-nearest-neighbors-classification.mp4 27MB
  7. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/03_key-concepts-in-machine-learning.mp4 24MB
  8. 03_module-3/01_module-3-evaluation/08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.mp4 20MB
  9. 04_module-4/01_module-4-supervised-machine-learning-part-2/08_data-leakage.mp4 19MB
  10. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/05_an-example-machine-learning-problem.mp4 19MB
  11. 02_module-2/01_module-2-supervised-machine-learning/01_introduction-to-supervised-machine-learning.mp4 19MB
  12. 02_module-2/01_module-2-supervised-machine-learning/08_linear-classifiers-support-vector-machines.mp4 18MB
  13. 02_module-2/01_module-2-supervised-machine-learning/04_k-nearest-neighbors-classification-and-regression.mp4 18MB
  14. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/02_introduction.mp4 17MB
  15. 04_module-4/01_module-4-supervised-machine-learning-part-2/03_random-forests.mp4 17MB
  16. 03_module-3/01_module-3-evaluation/05_multi-class-evaluation.mp4 17MB
  17. 02_module-2/01_module-2-supervised-machine-learning/07_logistic-regression.mp4 16MB
  18. 03_module-3/01_module-3-evaluation/02_confusion-matrices-basic-evaluation-metrics.mp4 16MB
  19. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/06_examining-the-data.mp4 16MB
  20. 02_module-2/01_module-2-supervised-machine-learning/02_overfitting-and-underfitting.mp4 16MB
  21. 02_module-2/01_module-2-supervised-machine-learning/11_cross-validation.mp4 13MB
  22. 04_module-4/01_module-4-supervised-machine-learning-part-2/02_naive-bayes-classifiers.mp4 12MB
  23. 02_module-2/01_module-2-supervised-machine-learning/10_kernalized-support-vector-machines.mp4 12MB
  24. 04_module-4/01_module-4-supervised-machine-learning-part-2/06_deep-learning-optional.mp4 11MB
  25. 02_module-2/01_module-2-supervised-machine-learning/09_multi-class-classification.mp4 10MB
  26. 03_module-3/01_module-3-evaluation/03_classifier-decision-functions.mp4 10MB
  27. 03_module-3/01_module-3-evaluation/06_regression-evaluation.mp4 10MB
  28. 04_module-4/01_module-4-supervised-machine-learning-part-2/04_gradient-boosted-decision-trees.mp4 9MB
  29. 03_module-3/01_module-3-evaluation/04_precision-recall-and-roc-curves.mp4 8MB
  30. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/04_python-tools-for-machine-learning.mp4 8MB
  31. 02_module-2/01_module-2-supervised-machine-learning/03_supervised-learning-datasets.mp4 7MB
  32. 04_module-4/01_module-4-supervised-machine-learning-part-2/01_introduction.mp4 5MB
  33. 03_module-3/01_module-3-evaluation/07_practical-guide-to-controlled-experiments-on-the-web.pdf 493KB
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  84. 02_module-2/01_module-2-supervised-machine-learning/13_a-few-useful-things-to-know-about-machine-learning_cacm12.pdf 156KB
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  98. .pad/42774 42KB
  99. 03_module-3/01_module-3-evaluation/01_model-evaluation-selection.en.srt 30KB
  100. .pad/30136 29KB
  101. 02_module-2/01_module-2-supervised-machine-learning/12_decision-trees.en.srt 28KB
  102. 04_module-4/01_module-4-supervised-machine-learning-part-2/05_neural-networks.en.srt 28KB
  103. 02_module-2/01_module-2-supervised-machine-learning/05_linear-regression-least-squares.en.srt 28KB
  104. 02_module-2/01_module-2-supervised-machine-learning/06_linear-regression-ridge-lasso-and-polynomial-regression.en.srt 27KB
  105. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/07_k-nearest-neighbors-classification.en.srt 26KB
  106. .pad/19428 19KB
  107. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/03_key-concepts-in-machine-learning.en.srt 19KB
  108. 03_module-3/01_module-3-evaluation/08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.en.srt 18KB
  109. 02_module-2/01_module-2-supervised-machine-learning/07_logistic-regression.en.srt 17KB
  110. 02_module-2/01_module-2-supervised-machine-learning/04_k-nearest-neighbors-classification-and-regression.en.srt 17KB
  111. 02_module-2/01_module-2-supervised-machine-learning/01_introduction-to-supervised-machine-learning.en.srt 17KB
  112. 04_module-4/01_module-4-supervised-machine-learning-part-2/03_random-forests.en.srt 17KB
  113. 04_module-4/01_module-4-supervised-machine-learning-part-2/08_data-leakage.en.srt 17KB
  114. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/02_introduction.en.srt 16KB
  115. 03_module-3/01_module-3-evaluation/02_confusion-matrices-basic-evaluation-metrics.en.srt 16KB
  116. 02_module-2/01_module-2-supervised-machine-learning/02_overfitting-and-underfitting.en.srt 16KB
  117. 02_module-2/01_module-2-supervised-machine-learning/08_linear-classifiers-support-vector-machines.en.srt 16KB
  118. 03_module-3/01_module-3-evaluation/05_multi-class-evaluation.en.srt 15KB
  119. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/05_an-example-machine-learning-problem.en.srt 15KB
  120. 02_module-2/01_module-2-supervised-machine-learning/11_cross-validation.en.srt 13KB
  121. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/06_examining-the-data.en.srt 12KB
  122. .pad/11821 12KB
  123. 04_module-4/01_module-4-supervised-machine-learning-part-2/02_naive-bayes-classifiers.en.srt 11KB
  124. 04_module-4/01_module-4-supervised-machine-learning-part-2/06_deep-learning-optional.en.srt 10KB
  125. 02_module-2/01_module-2-supervised-machine-learning/10_kernalized-support-vector-machines.en.srt 10KB
  126. 03_module-3/01_module-3-evaluation/03_classifier-decision-functions.en.srt 9KB
  127. 04_module-4/01_module-4-supervised-machine-learning-part-2/04_gradient-boosted-decision-trees.en.srt 8KB
  128. 02_module-2/01_module-2-supervised-machine-learning/09_multi-class-classification.en.srt 8KB
  129. .pad/8338 8KB
  130. 03_module-3/01_module-3-evaluation/06_regression-evaluation.en.srt 8KB
  131. 03_module-3/01_module-3-evaluation/04_precision-recall-and-roc-curves.en.srt 8KB
  132. 02_module-2/01_module-2-supervised-machine-learning/03_supervised-learning-datasets.en.srt 7KB
  133. 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/04_python-tools-for-machine-learning.en.srt 6KB
  134. .pad/5709 6KB
  135. 04_module-4/01_module-4-supervised-machine-learning-part-2/01_introduction.en.srt 5KB
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