StatsLearning,
in this course we will learn about the principles and techniques of Statistical Learning, which form the foundation of modern data science and machine learning. You'll explore both theoretical concepts and practical tools used to model relationships between variables, make predictions, and uncover patterns in data. Topics include linear and logistic regression, classification methods, resampling techniques, shrinkage (Ridge and Lasso), tree-based methods, support vector machines, and unsupervised learning like PCA and clustering. The course uses real datasets and examples to demonstrate how these methods are applied in practice, emphasizing understanding, interpretability, and statistical rigor. Whether you're a beginner in data science or aiming to strengthen your ML foundation, this course will guide you step by step using R or Python. No advanced math is required—just a basic understanding of statistics and a passion for learning data-driven techniques. Data School
