Machine Learning in R with tidymodels

Machine learning in R does not have to be a fragmented mix of different packages and inconsistent syntaxes. By adopting a unified framework, you can streamline your entire predictive modeling workflow from data preprocessing to model evaluation. This text-based course guides you through the modern tidymodels ecosystem, teaching you how to write clean, reproducible, and robust machine learning code. You will transition from basic data manipulation to building structured modeling pipelines that follow industry best practices. What you'll learn: - Understand the core philosophy and structure of the tidymodels framework in R - Prepare and preprocess data cleanly using recipes for feature engineering - Build and train diverse predictive models using the unified parsnip interface - Implement robust validation strategies using rsample for cross-validation - Tune model hyperparameters to optimize performance using tuning grids - Evaluate model performance using consistent yardstick metrics and workflows The course begins with foundational concepts of tidy data and machine learning principles, then progresses systematically through data splitting, preprocessing, model fitting, and hyperparameter tuning. You will read clear explanations and study structured code snippets designed to build your practical confidence. This program is designed for R users who want to learn machine learning or transition to the modern tidymodels framework, requiring only basic familiarity with R and no prior machine learning experience. Start reading today to build cleaner, more reliable machine learning workflows in R.