Supervised Machine Learning with R

Predicting outcomes from labeled data is a key task in many real-world analytics problems. This course will teach you how to build, evaluate, and interpret supervised learning models in R for both regression and classification tasks.

What you'll learn

Building accurate predictive models requires you to know how to choose and apply the right algorithms—it involves preparing data, selecting the right models, and understanding how to evaluate and communicate results. In this course, Supervised Machine Learning with R, you’ll gain the ability to train, evaluate, and interpret regression and classification models using R.

First, you’ll explore how to differentiate between regression and classification problems and prepare data using tools from the tidyverse, data.table, and rsample packages. Next, you’ll discover how to train and evaluate models like linear regression, logistic regression, and decision trees using performance metrics such as RMSE, R2, Accuracy, Precision, and Recall. Finally, you’ll learn how to compare models using cross-validation and interpret model behavior using techniques like SHAP values.

When you’re finished with this course, you’ll have the skills and knowledge of supervised learning needed to apply predictive modeling techniques effectively in R.