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

Welcome to the third section of the course. In this section we shall climb the heights of a fundamental machine learning concept: the decision tree. As a root stock for some of the most effective algorithms on the market, we take a look at the decision tree from the ground up. Applicable to both classification and regression problems, these basic tools are useful for interrogating the non-parametric decisions they produce.

Tree Thinking

We shall cover:

  • Heuristic demonstration behind decision tree logic.
  • Tree terminology: roots, leaves and splitting.
  • The machine learning approach.
  • Simple examples: classification and regression.
Next Lesson
17. Decision Trees in Everyday Thinking