1. Why?
2. L1 + intuition about its effect
3. L2 + intuition about its effect
4. L1 + L2 (short mention)
5. A worked-out example

1. Understand the various problems of Linear Regression
2. Learn about ways to handle Non-Linear Data
3. Understand Regularization and its types
4. Distinguish between L1 and L2
5. Understand Bias-Variance Trade-off
6. Learn about Model Validation

@gslides

Code Demonstration

Linear Regression sklearn

1. Ridge and Lasso in Python
2. Overview of Bias-Variance Trade-Off