Section 2
Support Vector Machines
7. Introducing: Support Vector Machines
07:44 (Preview)
8. Support Vector Machines to Maximise Decision Margins πŸ“‚
25:06
9. A Code Walkthrough for SVMs πŸ“‚
32:55
10. Overlapping Classes and Kernel SVMs πŸ“‚
21:06
11. Experimenting with Overlapping Class Distributions πŸ“‚
25:33
12. Using Kernel SVMs for Non-Linear Predictions πŸ“‚
11:36
13. Support Vector Machines in the Wild πŸ“‚
17:16
14. Solving Regression Problems with SVMs
22:37
15. Comparing Least-Squares with SVM Regression πŸ“‚
56:07
Section 3
Decision Trees
16. Introducing: Decision Trees
09:19 (Preview)
17. Decision Trees in Everyday Thinking πŸ“‚
20:29
18. Machine-Designed Decision Trees πŸ“‚
27:44
19. Classification Problems with Decision Trees: A Code Walkthrough πŸ“‚
25:55
20. Regression Problems with Decision Trees: A Code Walkthrough πŸ“‚
18:16
Section 4
Random Forests
21. Ensemble Methods: Machine Learning and Democracy
4:57 (Preview)
22. Random Forests: Decisions Don't Fall Far from the Decision Tree πŸ“‚
15:38
23. Random Forests out in the Wild πŸ“‚
36:15
24. Interpolation Through a Random Forest πŸ“‚
08:57
Section 5
Gradient Boosting
25. Give Yourself a Gradient Boost
07:01 (Preview)
26. Auto-Correction in a Forest of Stumps πŸ“‚
22:06
27. Gradient Boosting by Hand: Code Example πŸ“‚
15:55
28. XGBoost in the Wild πŸ“‚
14:41
29. Cross validate with the XGBoost API πŸ“‚
15:30
30. Conclusion, Certificate, and What Next?
05:52
11. Experimenting with Overlapping Class Distributions
Support Vector Machines
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12. Using Kernel SVMs for Non-Linear Predictions