Algorithm Foundations

- Lesson 1: How To Talk About Data in Machine Learning
- Lesson 2: Principle That Underpins All Algorithms
- Lesson 3: Parametric and Nonparametric Algorithms
- Lesson 4: Bias, Variance and the Trade-off

Linear Algorithms

- Lesson 5: Linear Regression

Lesson 6: Logistic Regression

Lesson 7: Linear Discriminant Analysis

Nonlinear Algorithms

- Lesson 8: Classification and Regression Trees
- Lesson 9: Naive Bayes
- Lesson 10: k-Nearest Neighbors
- Lesson 11: Learning Vector Quantization
- Lesson 12: Support Vector Machines

Ensemble Algorithms

- Lesson 13: Bagging and Random Forest
- Lesson 14: Boosting and AdaBoost