Machine Learning

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Here I will be adding resources that have Machine learning as their main topic. All levels will be added here. I will try to add the beginner ones towards the top.

Introduction to Machine Learning with the Tidyverse 💯

By Alison Hill & Garrett Grolemund

What is this?

Excerpt from site: This is the website for a two-day workshop offered January 27-28 at rstudio::conf 2020 in San Francisco, CA. This workshop provided a gentle introduction to machine learning and to tidymodels.

Link to workshop materials here: https://conf20-intro-ml.netlify.app/

Supervised Machine Learning case studies in R

By Julia Silge

What is this?

Excerpt from course: Predictive modeling, or supervised machine learning, is a powerful tool for using data to make predictions about the world around us. Once you understand the basic ideas of supervised machine learning, the next step is to practice your skills so you know how to apply these techniques wisely and appropriately. In this course, you will work through four case studies using data from the real world; you will gain experience in exploratory data analysis, preparing data so it is ready for predictive modeling, training supervised machine learning models using tidymodels, and evaluating those models.

Link to course: https://supervised-ml-course.netlify.app/

Supervised Machine Learning for Text Analysis in R

By Emil Hvitfeldt & Julia Silge

What is this?

Excerpt from e-book: This book serves as a thorough introduction to prediction and modeling with text, along with detailed practical examples.

Link to e-book: https://smltar.com/
Link to repo: https://github.com/EmilHvitfeldt/smltar

Tidymodels, Virtually: An Introduction to Machine Learning with Tidymodels

By Dr. Alison Hill

What is this?

Excerpt from site: 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.

Link to materials and course: https://tmv.netlify.app/site/