by Alison Hill and Garrett Grolemund

🗓️ January 27 and 28, 2020
⏰ 09:00 - 17:00
🏨 Continental Ballroom Room 5 (Ballroom Level)
✍️ https://rstd.io/conf20-intro-ml
🍱 Lunch in Grand Ballroom A (Grand Ballroom Level)

This workshop provides a gentle introduction to machine learning and to the tidyverse packages that do machine learning. You'll learn how to train and assess predictive models with several common machine learning algorithms, as well as how to do feature engineering to improve the predictive accuracy of your models. We will focus on learning the basic theory and best practices that support machine learning, and we will do it with a modern suite of R packages known as tidymodels. Tidymodels packages, like parsnip, recipes, and rsample provide a grammar for modeling and work seamlessly with R's tidyverse packages. Students should feel comfortable plotting with ggplot2 and using the dplyr and purrr packages. You can brush up on these topics by working through the online tutorials at https://rstudio.cloud/learn/primers.

Students will learn to train, assess, and generate predictions from several common machine learning methods with the tidymodels suite of packages.

This workshop is appropriate for attendees who answer yes to the questions below:

• Can you use mutate and purrr to transform a data frame that contains list columns?

• Can you use the ggplot2 package to make a large variety of graphs?

If you answered "no" to either question, you can brush up on these topics by working through the online tutorials at https://rstudio.cloud/learn/primers.

If you already use machine learning methods like random forests, neural networks, cross-validation or feature engineering, this course is NOT for you; register for Max Kuhn's Applied Machine Learning Workshop instead.

Please bring a laptop to class that has the following installed:

• A recent version of R (>=3.6.0), which is available for free at cran.r-project.org

• A recent version of RStudio Desktop (>=1.2.1500), available for free at www.rstudio.com/download (RStudio Desktop Open Source License)

• The R packages we will use, which you can install by connecting to the internet, opening RStudio, and running at the command line:

  install.packages(c("tidyverse", "tidymodels", "remotes", 
                      "rpart.plot", "rattle", "vip", "AmesHousing", 
                      "kknn", "rpart", "ranger", "partykit"))
  
  # and
  
  remotes::install_github(c("tidymodels/workflows", "tidymodels/tune", "tidymodels/modeldata"))
  

And don’t forget your power cord!

On the day of the class, we’ll provide you with an RStudio Server Pro login that contains all of the course materials. We will use the software listed above only as an important backup should there be problems with the classroom server connection.

Overview

Predicting

This work is licensed under a Creative Commons Attribution 4.0 International License.