Machine Learning in R: Clustering and Classification Fundamentals

Want to uncover hidden patterns in your data and make accurate predictions using R? Machine learning offers the tools to solve these complex problems, but getting started with the core algorithms can feel overwhelming. This text-based course guides you through the foundational concepts of supervised and unsupervised learning, specifically focusing on classification and clustering. You will gain the confidence to prepare datasets, build predictive models, and group complex data using modern R libraries. What you'll learn: - Understand the fundamental differences between supervised classification and unsupervised clustering. - Build robust classification models using decision trees, k-nearest neighbors, and logistic regression. - Group complex datasets using k-means and hierarchical clustering algorithms. - Apply modern tidymodels workflows to preprocess data and train models efficiently. - Evaluate model performance using key metrics like ROC curves, confusion matrices, and silhouette analysis. You will start with essential machine learning terminology and R syntax basics before moving step-by-step through practical data analysis scenarios. Each concept is reinforced with clear written explanations and structured code walk-throughs. This course is designed for beginners to data science and R programming; no prior machine learning experience is required. Start your journey into R machine learning today.