In our Linear Regression for machine learning course, you will learn the basics of the linear regression model and how to use linear regression for machine learning.

You'll learn how to select appropriate features for your linear regression model to yield the best performance. You'll also learn concepts such as gradient descent, an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent. And you’ll learn how to fit a model using the Ordinary Least Squares (OLS) algorithm, understand why OLS works, and practice some linear algebra along the way.

At the end of the course, you'll complete a project in which you will use Linear Regression to predict house sale prices using the AmesHousing data set. This project is a chance for you to combine the skills you learned in this course and practice a machine learning workflow. This project also serves as a portfolio project that you can showcase for future employers to show off your machine learning skills.By the end of this course, you'll be able to:

  • Make predictions using the linear regression machine learning model.
  • Select, clean, and transform features.
  • Apply two different ways of fitting a linear regression model.

Learn Linear Regression for Machine Learning

The Linear Regression Model

Learn how to use linear regression for machine learning.

Feature Selection

Learn how to select features for linear regression.

Gradient Descent

Learn how to fit a model using gradient descent.

Ordinary Least Squares

Learn how to fit a model using OLS.

Processing and Transforming Features

Learn how to clean and prepare features for linear regression.

Predicting House Sale Prices

Practice building and improving linear regression models.