Machine Learning in R (Part I)
Chris Kennedy & Nutsa Abazadze
Data Scientist, Berkeley Institute for Data Science
This workshop will introduce the fundamentals of applied machine learning using R. Using
interactive R Markdown notebooks, you will explore key procedures like data preparation and
cross-validation, along with intuition and sample code for the most popular machine learning algorithms:
lasso, decision trees, random forest, xgboost, and SuperLearner ensembles. You will be empowered to apply
machine learning to your own data and will have resources for self-study.
You will need a laptop with R 3.5+, RStudio. You ashould also have an understanding of basic R, including installing packages and manipulating data frames.