Module 17: Create and Understand Regression Models with Python

In this exercise, you’ll dive into the world of regression models in machine learning using Python. Through practical exercises, you'll gain insights into various regression techniques and their applications in predictive modeling.

Exercise Overview

In this module, you will learn:

• Work with new algorithms: Linear regression, multiple linear regression, and polynomial regression.
• Understand the strengths and limitations of regression models.
• Visualize error and cost functions in linear regression.
• Understand basic evaluation metrics for regression.

This module provides a comprehensive introduction to regression modeling, allowing you to explore different algorithms, understand their underlying principles, and evaluate their performance.

Jupyter Notebooks

To get started, access the following Jupyter notebooks:

1. Training a Simple Linear Regression Model - Learn the fundamentals of linear regression by training a simple linear regression model on a dataset with one predictor variable.
2. Training a Multiple Linear Regression Model - Extend your knowledge to multiple linear regression by training a model on a dataset with multiple predictor variables.
3. Polynomial Regression - Explore polynomial regression, a technique to capture non-linear relationships between variables, by training a model on polynomial features.

These notebooks will guide you through practical exercises where you'll apply regression algorithms, visualize model performance, and gain a deeper understanding of regression techniques in machine learning.

Happy learning!