StatsLearning basics, in this course we will learn about StatsLearning basics, which form the foundation of statistical learning used in data analysis and predictive modeling. You will explore the core concepts of supervised learning, including linear regression, logistic regression, and classification techniques. The course will also introduce you to unsupervised learning methods such as clustering and principal component analysis (PCA). You’ll gain a solid understanding of how to evaluate model performance using cross-validation and apply regularization techniques like ridge and lasso regression to prevent overfitting. Through hands-on exercises in R or Python, you’ll learn to implement these methods on real datasets. Whether you're starting a journey in data science or looking to strengthen your statistical foundations, this course provides a clear, structured, and practical introduction to the key techniques and thinking behind statistical learning. Data School
