Applied Machine Learning Workshop
This article is originally published at https://www.rstudio.com/blog/
Join Max Kuhn of RStudio for his popular Applied Machine Learning Workshop in Washington D.C.! If you’d missed his sold out course at rstudio::conf 2018 now is your chance.
Register here: https://www.rstudio.com/workshops/applied-machine-learning/
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 high-level approaches to modeling (e.g., the caret package) and newer modeling packages in the tidyverse: recipes, rsample, yardstick, and tidyposterior. Basic familiarity with R and the tidyverse is required.
This course is taught by Dr. Max Kuhn a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, Cubist, C50 and others. He routinely teaches classes in predictive modeling at rstudio::conf, Predictive Analytics World, and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology. He and Kjell Johnson wrote the award-winning book Applied Predictive Modeling in 2013.
When - 8 a.m.to 5 p.m. Wednesday, August 15th and Thursday, August 16th
Where - 20F Conference Center, 20 F Street NW, Suite 1000, Washington D.C.
Who - Dr. Max Kuhn
Register here: https://www.rstudio.com/workshops/applied-machine-learning/
Discounts are available for 5 or more attendees from any organization. Email [email protected] if you have any questions about the workshop that you don’t find answered on the registration page.
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This article is originally published at https://www.rstudio.com/blog/
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