1. Introduction to Supervised Machine Learning and Linear Regression
2. Data Splits and Cross Validation
3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
4. Logistic Regression
5. K Nearest Neighbors
6. Support Vector Machines
7. Decision Trees
8. Ensemble Models
9. Modeling Unbalanced Classes