Machine Learning with R

Learn the fundamentals and application of modern machine learning tasks. This course will cover unsupervised techniques to discover the hidden structure of datasets along with supervised techniques for predicting categorical and numeric responses via classification and regression.

Techniques covered in the session include:

  • Unsupervised
  • Principal components analysis
  • Clustering
  • Supervised regression techniques
  • Model validation
  • Linear regression and its cousins
  • Nonlinear regression
  • Regression trees
  • Supervised classification techniques
  • Model validation
  • Linear classification models
  • Nonlinear classification models
  • Classification trees

Learn how to process data for modeling, how to train your models, how to visualize your models and assess their performance, and how to tune their parameters for better performance. The course emphasizes intuitive explanations of the techniques while focusing on problem-solving with real data across a wide variety of applications. This is a hands-on course so bring your laptop!

Intended audience

This course is intended for academics and data science practitioners who wish to learn about machine learning tasks as well as a guide to applying them. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis, along with intermediate R programming skills.

Course fee:

$750, includes breakfast, lunch, snacks, and free parking for both days.


"Brad was a fantastic instructor. I loved the course content and the overall structure."

"Brandon gave a masterclass in R! I have never attended a better session than this."

"Tips and tricks are worth the price of admission."

"The scripts and explanation are too good; the excellence of this course cannot be found anywhere else."

"Very intense and extremely helpful."

"The instructor was extremely clear in explaining, had an in-depth knowledge of the material, and kept it interesting and relateable."


Brad Boehmke, PhD, is the Director of Data Science at 84.51°, a professor at three universities, author of Data Wrangling with R, and creator of multiple R open source packages and data science short courses. He focuses on developing algorithmic processes, solutions, and tools that enable 84.51° and its analysts to efficiently extract insights from data and provide solution alternatives to decision-makers. He has a wide analytic skill set covering descriptive, predictive, and prescriptive analytic capabilities applied across multiple domains including retail, healthcare, cyber intelligence, finance, the Department of Defense, and aerospace. Summary of his works is available online.

Brandon Greenwell, PhD, is a Senior Data Scientist for Ascend Innovations and Adjunct Professor of Statistics at Wright State University. He is the author of several R packages on CRAN and has published several articles, including two in The R Journal, and is currently co-authoring an R book with Brad Boehmke, Advanced Business Analytics with R: Description, Prediction, and Prescription, to be published by CRC Press in early 2019.