Course overview

In our Linear Regression Modeling in R course, you will learn the basics of the linear regression model and how to use linear regression for predictions and inferences.

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:

  • Understand the fundamentals of predictive modeling.
  • Build linear regression models.
  • Interpret linear regression models.
  • Assess model fit and accuracy.

Key skills

Course outline

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Projects in this course

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