Regression

Estimated time: 2 hours

Read the following sections from Chapter 4 of Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow:

• Linear Regression
• Polynomial Regression

The goal is to understand how regression models learn to predict continuous values, such as prices or temperatures, and how polynomial features allow a linear model to represent more flexible relationships.

For this reading, design your own three-pass approach.

First Pass: Orient Yourself

Skim the headings, figures, equations, and code examples to understand the main ideas and structure.

Second Pass: Read for Understanding

Read the two sections carefully.

Focus on:

• what a regression model is trying to predict;
• how linear regression represents relationships between features and a target;
• how a model is fitted to data;
• why polynomial features allow more complex relationships to be represented.

Third Pass: Explore

Return to the parts you found most important or difficult.

You may also reproduce or modify one of the examples from the chapter in a notebook.

Goal: Understand the core idea behind linear and polynomial regression rather than memorizing their implementation.