Introduction to Machine Learning in R: Part 2

This is a six-hour tutorial on machine learning in R that covers data preprocessing, cross-validation, ordinary least squares regression, lasso, decision trees, random forest, xgboost, and superlearner algorithms. These methods that are important across scientific disciplines for computational investigation of virtually all academic research questions and can help you gain an edge for employment in university, business, industry, and technology settings. 

Prior knowledge requirements: R FUN!damentals: Parts 1 through 4 or previous intermediate working knowledge of R.

**Please install the necessary packages before the date of the workshop - instructions in the link below**