Supervised Machine Learning: Regression Algorithms with Python

Frame a Regression Experiment

Interpret a Simple Linear Regression Equation

Understand Least Squares and Prediction Residuals

Trace Gradient Descent With a Small Numeric Example

Fit and Interpret Multiple Linear Regression

Choose Regression Metrics and Compare a Baseline

Read Residual Diagnostics and Check Assumptions

Encode Categories Inside a Regression Pipeline

Add Polynomial Features Without Leaking Validation Data

Control Coefficients With Ridge and Lasso

Select a Regression Workflow With Cross-Validation

Capstone: Deliver a Reviewed Price Prediction Model