Machine Learning in R Part I – Jared Lander

Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today’s incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and examines some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model and its generalization, the glm. We look at how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Viewers should have a good understanding of linear models and classification and should have R and RStudio installed, along with the `glmnet`, `xgboost`, `boot`, `ggplot2`, `UsingR` and `coefplot` packages.