Learn Applied Machine Learning in R

Learn modern machine learning with the creator of the caretpackage, Max Kuhn.

This two-day workshop will step through the process of building, visualizing, testing and comparing models that are focused on prediction. The goal of the workshop is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies are used to illustrate functionality. Basic familiarity with R is required.

By the end of this workshop, you should be able to easily build predictive/machine learning models in R using a variety of packages and model types.

Participants will receive a copy of Max Kuhn's book, Applied Predictive Modeling.

Before class everyone should install R, RStudio and the following packages.

• caret
• doParallel
• earth
• glmnet
• klaR
• recipes
• rpart
• rsample
• tidyposterior
• yardstick

The workshop is broken into five parts.

Part 1 Introduction

Part 2 Basic Principals: Data Splitting, Models in R, Resampling, Tuning

Part 3 Feature Engineering and Preprocessing: Data treatments

Part 4 Regression Modeling: Measuring Performance, penalized regression, multivariate adaptive regression splines (MARS), ensembles

Part 5 Classification Modeling: Measuring Performance, trees, ensembles, naive Bayes