The performance of machine learning models isn't just about the type of model you use or the code you write. As any model's performance is a direct consequence of the features it's fed, placing domain knowledge at the center of the process is vital.

In this live training, Jorge Zazueta will teach you how to manually engineer features, build and assess a machine learning workflow and automate feature transformation using recipes. These ideas will help you make your models more efficient, interpretable, and accurate!

What will I learn?

Learn to engineer features, and tactics to decide which features to engineer.
Learn how to evaluate machine learning model performance (and see how changing features changes performance).
Learn to use tidymodels recipes to transform features automatically.
Code along with us!

Challenge workspace: http://bit.ly/3Urhia4

Solution workspace: http://bit.ly/3zM7Dl5

Download the slides here: http://bit.ly/3mssmav

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