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:

## Learn Linear Modeling with R

### Introduction to Modeling

Learn the fundamental concepts behind modeling with R

### Bivariate Relationships - Correlation and Scatterplots

Learn about correlation and using scatterplots to understand bivariate relationships for linear regression with R.

### Estimating the Coefficients and Fitting Linear Models

Learn about estimating coefficients and fitting a linear regression model with R.

### Assessing the Accuracy of the Model

Learn about assessing the accuracy of a linear regression model with R.

### Fitting Many Linear Models

Learn about fitting and visualizing many linear regression models with R.

### Guided Project: Predicting Condominium Sale Prices

Practice building and improving linear regression models with R.