Section 2
Support Vector Machines
7. Introducing: Support Vector Machines
07:44 (Preview)
8. Support Vector Machines to Maximise Decision Margins šŸ“‚
25:06
9. A Code Walkthrough for SVMs šŸ“‚
32:55
10. Overlapping Classes and Kernel SVMs šŸ“‚
21:06
11. Experimenting with Overlapping Class Distributions šŸ“‚
25:33
12. Using Kernel SVMs for Non-Linear Predictions šŸ“‚
11:36
13. Support Vector Machines in the Wild šŸ“‚
17:16
14. Solving Regression Problems with SVMs
22:37
15. Comparing Least-Squares with SVM Regression šŸ“‚
56:07
Section 3
Decision Trees
16. Introducing: Decision Trees
09:19 (Preview)
17. Decision Trees in Everyday Thinking šŸ“‚
20:29
18. Machine-Designed Decision Trees šŸ“‚
27:44
19. Classification Problems with Decision Trees: A Code Walkthrough šŸ“‚
25:55
20. Regression Problems with Decision Trees: A Code Walkthrough šŸ“‚
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 šŸ“‚
15:38
23. Random Forests out in the Wild šŸ“‚
36:15
24. Interpolation Through a Random Forest šŸ“‚
08:57
Section 5
Gradient Boosting
25. Give Yourself a Gradient Boost
07:01 (Preview)
26. Auto-Correction in a Forest of Stumps šŸ“‚
22:06
27. Gradient Boosting by Hand: Code Example šŸ“‚
15:55
28. XGBoost in the Wild šŸ“‚
14:41
29. Cross validate with the XGBoost API šŸ“‚
15:30
30. Conclusion, Certificate, and What Next?
05:52
10. Overlapping Classes and Kernel SVMs
Support Vector Machines
šŸ“‚ Please register or log in to download resources
Next Lesson
11. Experimenting with Overlapping Class Distributions