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Explore the powerful caret package for machine learning in R through this comprehensive tutorial. Learn to create stratified random samples, train models using a common interface, leverage cross-validation and hyperparameter tuning features, and scale operations with multi-core parallel training. Discover how to accelerate your data science workflow by implementing common tasks across the project lifecycle using caret's extensive capabilities, which support over 200 machine learning algorithms. Gain practical knowledge through hands-on examples and access accompanying code and datasets to enhance your R programming skills for data science projects.
