Learn about the most important linear regression models for machine learning: OLS, Ridge, Lasso, ElasticNet, LARS, Logistic Regression, Polynomial Regression

Understand their Cost function, the mathematic behind and how it is optimized

Discover the optimization algorithms like Gradient and Coordinate descent

Understand what regularization means (L1 and L2 penalties), the intuition behind.

Learn the different types of regularization, understand and visualize the difference between them

Understand how to evaluate the models 

Practice all way with Python, and learn how to prepare your dataset, how to fit each model, to evaluate it and visualize it using scikit-learn and other libraries

Understand how to tune hyperparameters and how to use cross validation

Dig into details for some models to visualize and master the penalty parameter and how it influences the model