This workshop provides an introduction to machine learning with R using the tidymodels framework, a collection of packages for modeling and machine learning using tidyverse principles. We will build, evaluate, compare, and tune predictive models. Along the way, we'll learn about key concepts in machine learning including overfitting, resampling, 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.
This course assumes intermediate R knowledge. If you can use the magrittr pipe and tidyverse functions from packages like readr, dplyr, tidyr, and ggplot2 to read data into R, transform and reshape data, and make a wide variety of graphs, this tutorial is for you. We expect participants to have some exposure to basic statistical concepts, but NOT intermediate or expert familiarity with modeling or machine learning.
Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.
Max Kuhn is a software engineer at RStudio. He is currently working on improving R’s modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of a number of R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their new book, Feature Engineering and Selection, was released in 2019.
David Robinson is a data scientist at Heap. His interests include statistics, data analysis, education, and programming in R.
David is the co-author with Julia Silge of the tidytext package and the O'Reilly book Text Mining with R. David is also the author of the broom and fuzzyjoin packages, and of the e-book Introduction to Empirical Bayes.
David previously worked as Chief Data Scientist at DataCamp and as a data scientist at Stack Overflow, and received a PhD in Quantitative and Computational Biology from Princeton University.
