Machine Learning
ML powers modern quant strategies. You'll learn regression, trees, boosting, and neural networks. I show you the bias-variance tradeoff, feature engineering, and how these models apply to trading.
30 lessons
33 min
Lessons
1. Intro
2. Supervised vs Unsupervised
The two main paradigms
3m
3. Linear and Logistic Regression
The workhorses of quant ML
4m
4. Bias-Variance Tradeoff
The core concept of model selection
4m
5. Quiz: Bias-Variance
Test your understanding
2m1 problems
6. Regularization
Preventing overfitting
4m
7. Cross-Validation
Proper model evaluation
4m
8. Feature Engineering
Creating predictive inputs
4m
9. Quiz: Regularization
Test your understanding
2m1 problems
10. Decision Trees
11. Random Forests
12. Gradient Boosting
13. Quiz: Cross-Validation
Test your understanding
2m1 problems
14. Support Vector Machines
15. K-Nearest Neighbors
16. Clustering Methods
17. Quiz: Feature Engineering
Test your understanding
2m1 problems
18. Dimensionality Reduction
19. PCA Deep Dive
20. Neural Network Basics
21. Quiz: Model Selection
Test your understanding
2m1 problems