O'Reilly - Advanced Machine Learning with scikit learn

1. 01. Introduction/01_01-What To Expect And About The Author.mp412.51MB
2. 01. Introduction/01_02-Setup.mp45.86MB
3. 01. Introduction/01_03-The Classifier Interface.mp427.18MB
4. 01. Introduction/01_04-The Regressor Interface.mp410.81MB
5. 01. Introduction/01_05-The Transformer Interface.mp47.9MB
6. 01. Introduction/01_06-The Cluster Interface.mp422.34MB
7. 01. Introduction/01_07-The Manifold Interface.mp410.96MB
8. 01. Introduction/01_08-scikitLearn Interface Summary.mp411.31MB
9. 01. Introduction/01_09-CrossValidation With Cross_Val_Score.mp420.32MB
10. 01. Introduction/01_10-Parameter Searches With GridSearchCV.mp419.34MB
11. 01. Introduction/01_11-How To Access Your Working Files.mp426.38MB
12. 02. Model Complexity, Overfitting And Underfitting/02_01-What Is Model Complexity And Overfitting.mp47.25MB
13. 02. Model Complexity, Overfitting And Underfitting/02_02-Linear Models InDepth.mp433.58MB
14. 02. Model Complexity, Overfitting And Underfitting/02_03-Kernel SVMs InDepth.mp421.89MB
15. 02. Model Complexity, Overfitting And Underfitting/02_04-Random Forests InDepth.mp415.13MB
16. 02. Model Complexity, Overfitting And Underfitting/02_05-Learning Curves For Analyzing Model Complexity.mp412.64MB
17. 02. Model Complexity, Overfitting And Underfitting/02_06-Validation Curves For Analyzing Model Parameters.mp47.61MB
18. 02. Model Complexity, Overfitting And Underfitting/02_07-Efficient Parameter Search With EstimatorCV Objects.mp418.08MB
19. 03. Pipelines/03_01-Motivation Of Using Pipelines.mp49.69MB
20. 03. Pipelines/03_02-Defining A Pipeline And Basic Usage.mp419.08MB
21. 03. Pipelines/03_03-CrossValidation With Pipelines.mp47.76MB
22. 03. Pipelines/03_04-Parameter Selection With Pipelines.mp416.74MB
23. 04. Advanced Metrics And Imbalanced Classes/04_01-Be Mindful Of Default Metrics.mp420.6MB
24. 04. Advanced Metrics And Imbalanced Classes/04_02-More Evaluation Methods For Classification.mp414.57MB
25. 04. Advanced Metrics And Imbalanced Classes/04_03-AUC.mp419.69MB
26. 04. Advanced Metrics And Imbalanced Classes/04_04-Defining Custom Metrics.mp420.44MB
27. 05. Model Selection For Unsupervised Learning/05_01-Guidelines For Unsupervised Model Selection.mp421.63MB
28. 05. Model Selection For Unsupervised Learning/05_02-Model Selection For Density Models.mp418.17MB
29. 05. Model Selection For Unsupervised Learning/05_03-Model Selection For Clustering.mp414.02MB
30. 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_01-Why Real Data Is Messy.mp419.04MB
31. 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_02-OneHot Encoding For Categorical Data.mp418.12MB
32. 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_03-Working With Dictionaries.mp46.42MB
33. 06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data/06_04-Handling Incomplete Data.mp414.68MB
34. 07. Handling Text Data/07_01-Motivation.mp48.03MB
35. 07. Handling Text Data/07_02-BagOfWords Representations.mp419.1MB
36. 07. Handling Text Data/07_03-Text Classification For Sentiment Analysis Part 1.mp425.36MB
37. 07. Handling Text Data/07_04-Text Classification For Sentiment Analysis Part 2.mp412.97MB
38. 07. Handling Text Data/07_05-The Hashing Trick.mp49.11MB
39. 07. Handling Text Data/07_06-Other Representations Distributed Word Representations.mp45.08MB
40. 08. Out Of Core Learning/08_01-The TradeOffs Of Out Of Core Learning.mp410.8MB
41. 08. Out Of Core Learning/08_02-The scikitLearn Interface For Out Of Core Learning.mp414.67MB
42. 08. Out Of Core Learning/08_03-Kernel Approximations For LargeScale NonLinear Classification.mp416.04MB
43. 08. Out Of Core Learning/08_04-Subsample And Transform Supervised Transformations For Out Of Core Learning.mp418.35MB
44. 08. Out Of Core Learning/08_05-Application OutOfCore Text Classification.mp418.4MB
45. 09. Conclusion/09_01-Summary.mp410.07MB
46. 09. Conclusion/09_02-Where To Go From Here.mp48.62MB