This four-hour workshop will provide a gentle introduction to machine learning with R using the modern suite of predictive modeling packages called tidymodels . We will build, evaluate, compare, and tune predictive models. Along the way, we’ll learn about key concepts in machine learning including overfitting, the holdout method, the bias-variance trade-off, ensembling, cross-validation, and feature engineering. Learners will gain knowledge about good predictive modeling practices, as well as hands-on experience using tidymodels packages like parsnip , rsample , recipes , yardstick , tune , and workflows .