Start your machine learning journey and learn the most fundamental building blocks of the tidymodels framework.

Learn why data preprocessing is crucial in your machine learning workflow and create your first data transformations with the recipes package.

Learn how to fit machine learning models using the parsnip package including linear regression, decision trees and boosting trees.

Learn how to estimate model quality based on different performance metrics using the yardstick package.

Avoid overfitting by using resampling techniques including cross-validation and bootstrap using the rsample package.

Optimize your model parameters using the tune package to find models which predict new data well.