Statistical learning,
in this course we will learn about Statistical Learning, a key field that combines statistics and machine learning to analyze and model complex data. You will begin with the foundational concepts of supervised and unsupervised learning, understanding how statistical models like linear regression, logistic regression, and classification trees work. The course will guide you through essential techniques such as cross-validation, regularization (including ridge and lasso regression), and model selection. You will also explore unsupervised methods like clustering and principal component analysis (PCA). Using tools like R and Python, you'll apply these methods to real datasets and learn how to evaluate model performance. Emphasis is placed on both theoretical understanding and hands-on practice. Whether you're interested in data science, analytics, or predictive modeling, this course will provide the knowledge and skills needed to build accurate and interpretable statistical models. Data School
