Machine Learning in R with Tidymodels

Building machine learning workflows in R can often feel fragmented with different packages requiring completely different syntaxes. This comprehensive text-based course guides you through tidymodels, a cohesive collection of packages that makes modeling in R consistent, reproducible, and efficient. You will transition from writing disorganized modeling code to constructing clean, production-ready machine learning pipelines. What you'll learn: - Understand the core philosophy of the tidymodels framework and how its components fit together - Preprocess and engineer features cleanly using the recipes package - Specify and fit diverse model types consistently using the parsnip package - Evaluate model performance accurately using yardstick and cross-validation techniques - Tune model hyperparameters efficiently using tune and dial packages - Apply modern tidyverse principles to keep your machine learning workflows organized and readable This course starts with foundational concepts, guiding you through basic statistical definitions and tidy data principles before diving into practical modeling workflows. You will progress from simple linear models to tuning complex machine learning algorithms, validating your understanding through written exercises and code analysis. This course is designed for beginners to machine learning in R, data analysts looking to modernize their modeling workflow, and R users familiar with basic tidyverse tools. No prior machine learning experience is required. Start reading today to build cleaner, more reliable machine learning models in R.