Machine Learning Science with R

Machine learning is transforming industries, and R remains one of the most powerful languages for statistical modeling and data analysis. This text-based course guides you through the essential concepts and code needed to build, evaluate, and deploy machine learning models. You will transition from basic data manipulation to designing sophisticated predictive models. By reading detailed explanations, analyzing structured code snippets, and working through conceptual exercises, you will master the workflows used by professional data scientists. What you'll learn: Understand foundational machine learning concepts, including the difference between supervised and unsupervised learning; Prepare and preprocess raw data using modern R workflows and feature engineering techniques; Train and tune predictive models using the powerful tidymodels ecosystem; Evaluate model performance using key metrics, cross-validation, and validation techniques; Explore advanced topics such as basic natural language processing and scalable machine learning principles. The course starts with core definitions and statistical foundations before progressing to hands-on modeling techniques. You will explore regression, classification, clustering, and hyperparameter tuning through comprehensive written guides. This course is designed for aspiring data scientists and analysts who have a basic familiarity with R and want to specialize in machine learning. No prior machine learning experience is required. Start reading today to build your machine learning toolkit with R.