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In this workshop, we provide an introduction to machine learning algorithms by making use of the tidymodels package. First, we discuss what machine learning is, what problems it works well for, and what problems it might work less well for. Then, we’ll explore the tidymodels framework to learn how to fit machine learning models in R. Finally, we will apply the tidymodels framework to explore multiple machine learning algorithms in R.
By the end of the workshop, learners should feel prepared to explore machine-learning approaches for their data problems.
Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.
Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed and basic familiarity with the tidyverse.
Workshop Materials: https://github.com/dlab-berkeley/R-Machine-Learning
Software Requirements: Installation Instructions for getting started with using R and RStudio.