Video Highlights: Maching Learning in R – Three Reasons to Use tidymodels

The following video presentation comes from my favorite Meetup group “LA R users group“, a 2,200+ member group that puts on some amazing virtual presentations.

This talk centers around that fact that modeling and machine learning in R involve a bewildering array of heterogeneous packages, and establishing good statistical practice is challenging in any language.

The tidymodels collection of packages offers a consistent, flexible framework for your modeling and machine learning work to address these problems.

The talk focuses on three specific reasons to consider using tidymodels – starting with model characteristics themselves, moving to the wise management of your data budget, and finishing with feature engineering.

The lecture is given by Julia Silge, a data scientist and software engineer at RStudio where she works on open source modeling tools.

She is both an international keynote speaker and a real-world practitioner focusing on data analysis and machine learning practice, and is the author of “Text Mining with R” with her coauthor David Robinson.

She loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

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