Tidymodels, Virtually

An Introduction to Machine Learning with Tidymodels

Alison Hill, Ph.D. https://alison.rbind.io (RStudio, PBC)https://rstudio.com

Table of Contents


Thank you for enrolling in this course! These are the materials for an introductory machine learning short course with tidymodels by Dr. Alison Presmanes Hill.

This four-hour pre-conference short course 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.

The entire course will be recorded and made available to registered R/Medicine 2020 conference participants for replay. To protect the privacy of participants, no breakouts, video feeds, or chats will be recorded. We also request that you refrain from recording or screen-grabbing any part of the course.




Please tune into class with a laptop that has the following installed:

I look forward to meeting you,




Upcoming and past offerings:


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Text and figures are licensed under Creative Commons Attribution CC BY-SA 4.0. Source code is available at https://github.com/rstudio-education/tidymodels-virtually, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".