Machine Learning with Tidymodels in R

Machine learning in R can often feel fragmented with different packages requiring completely different syntax and structures. The tidymodels ecosystem solves this by providing a cohesive, modular framework that aligns perfectly with tidyverse principles. This written course helps you transition from writing disjointed modeling scripts to crafting clean, reproducible, and modern machine learning workflows. You will learn to prepare data, train models, evaluate performance, and tune hyperparameters using a consistent and elegant R codebase. What you'll learn: Understand the core philosophy of tidymodels and how it integrates with the tidyverse; Preprocess and engineer features cleanly using the recipes package; Train and compare diverse model types using a unified interface with parsnip; Evaluate model performance using robust resampling methods like cross-validation; Tune model hyperparameters to optimize predictive performance; Apply modern best practices for reproducible workflows and clean code structure. The course begins with foundational concepts of modeling and the structure of the tidymodels ecosystem. From there, you will progress step-by-step through data preprocessing, model fitting, evaluation, and tuning, building your confidence through clear written explanations and practical code examples. This course is designed for beginners to machine learning in R who have a basic familiarity with R and the tidyverse, requiring no prior modeling experience. Start reading today to build cleaner, more reliable machine learning models in R.