This course begins with a basic introduction, and describes why decision trees are useful tools and how they differ from more traditional analytical tools like linear regression. We’ll also cover some basics of R, as the examples in this course will use the R programming language to analyze data. The second module then dives into so-called “regression trees”, or decision trees for continuous variables (i.e. variables that take on numeric values, like sales amounts or number of purchases). It provides a theoretical basis for these models as well as practical examples and use cases. The third module is very similar to the second, except that it treats categorical variables (i.e. product type of next purchase) instead of continuous variables. In the fourth module, we’ll talk about random forests and the idea of combining many individual classification or regression trees to make one final, improved prediction. Module 5 builds on the idea of random forests, but presents a slightly different framework with boosted trees. You’ll learn about an implementation of boosted trees, XGBoost, which is one of the most popular tree algorithms and has been used extensively for machine learning problems.