by Max Kuhn
INSTRUCTIONS FOR INSTRUCTORS: Please insert information about your workshop below. Then, add workshop content in the materials folder and link to each session’s materials from the schedule below. You are welcomed to add more rows to the schedule. We just ask that you take breaks at the specified times. Once you are done adding information, you can remove these instructions from the README.
🗓️ January 27 and 28, 2020
⏰ 09:00 - 17:00
🏨 [ADD ROOM]
✍️ rstd.io/conf
Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast-growing fields of research in the world of data science.
This two-day course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing, and comparing models that are focused on prediction.
The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies on real data will be used to illustrate the functionality and several different predictive models are illustrated. The course focuses on both low- and high-level approaches to modeling using the tidyverse and uses several types of models for illustration.
Attendees will be able to use the tidymodels packages to create, tune, fit, visualize, and assess models created for the purpose of prediction.
This course requires basic familiarity with R and the tidyverse.
[ADD INFORMATION YOU WANT LEARNERS TO HAVE / STEPS THEY WANT THEM TO COMPLETE PRIOR TO THE WORKSHOP. THIS COULD BE A LINK TO A THREAD ON RSTUDIO COMMUNITY, PACKAGE INSTALL INSTRUCTIONS, HOW TO GET AN RSTUDIO.CLOUD ACCOUNT, ETC.]
| Time | Activity |
|---|---|
| 09:00 - 10:30 | Session 1 |
| 10:30 - 11:00 | Coffee break |
| 11:00 - 12:30 | Session 2 |
| 12:00 - 13:30 | Lunch break |
| 13:30 - 15:00 | Session 3 |
| 15:00 - 15:30 | Coffee break |
| 15:30 - 17:00 | Session 4 |
[ADD INSTRUCTOR BIO]
