Introduction to machine learning with {tidymodels} in R

This professional development workshop will introduce you to using {tidymodels} for machine learning in R.

By Nicola Rennie in Conference

Abstract

Machine learning (ML) is an important aspect of data science that can be used to create predictions, make classifications, and uncover insights in data that can be difficult to detect. {tidymodels} is a collection of R packages that can be used for various aspects of machine learning pipelines, including sampling data, building and fitting models, and performance evaluation. The {tidymodels} framework provides a consistent, user-friendly approach to fitting machine learning models in R.

This interactive workshop will introduce some common machine learning techniques such as Lasso regression, random forests and support vector machines, and demonstrate how to fit these models in R using {tidymodels}. We’ll also cover some of the common concepts of machine learning such as cross-validation, hyperparameter tuning and model evaluation.

Prerequisites

Please bring a laptop with internet access to the workshop.

No previous knowledge of machine learning is required for this workshop, though familiarity with some statistical concepts such as correlation, variability, and simple linear regression may be helpful. Being reasonably comfortable with data wrangling using {dplyr} and {tidyr} would be beneficial to attendees.

The following packages will be used in the workshop:

tidymodels
glmnet
ranger
openintro (for data)
dplyr, tidyr, ggplot2, forcats (optional)

Introduction to machine learning with {tidymodels} in R

Abstract

Prerequisites

Details

Resources

Further reading