Modeling with tidymodels in R

Streamlined Machine Learning Workflows in R

The tidymodels framework brings consistency and modularity to machine learning in R. This intermediate course guides you through building structured ML workflows—from data preprocessing and model specification to hyperparameter tuning and model evaluation.

Who Is This Course For?

Suited for data analysts and R programmers looking to standardize their machine learning pipelines using the modern tidymodels ecosystem.

Key Takeaways

• Data Preprocessing with Recipe: Prepare and transform features systematically using tidymodels' recipe package.
• Validation & Tuning: Split datasets for cross-validation and fine-tune model hyperparameters to improve accuracy.
• Practical Applications: Apply modeling workflows to solve real problems, such as predicting housing prices and classifying employee retention risk.