Textbook in PDF format "Core Concepts in Statistical Learning" serves as a comprehensive introduction to fundamental techniques and concepts in statistical learning, tailored specifically for undergraduates in the United States. This book covers a broad range of topics essential for students looking to understand the intersection of statistics, Data Science, and Machine Learning. The book explores major topics, including supervised and unsupervised learning, model selection, and the latest algorithms in predictive analytics. Each chapter delves into methods like decision trees, neural networks, and support vector machines, ensuring readers grasp theoretical concepts and apply them to practical data analysis problems. Designed to be student-friendly, the text incorporates numerous examples, graphical illustrations, and real-world data sets to facilitate a deeper understanding of the material. Structured to support both classroom learning and self-study, it is a versatile resource for students across disciplines such as economics, biology, engineering, and more. Whether you're an aspiring data scientist or looking to enhance your analytical skills, "Core Concepts in Statistical Learning" provides the tools needed to navigate the complex landscape of modern data analysis and predictive modeling. Examples in Python. Introduction to Statistical Learning Linear Regression Classification Logistic Regression Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Naive Bayes Classifier k-Nearest Neighbors (kNN) Support Vector Machines (SVMs) Decision Trees Ensemble Methods (Bagging, Boosting) Evaluating Classification Models Model Selection and Regularization Resampling Methods Kernel Methods Tree-Based Methods Unsupervised Learning Neural Networks and Deep Learning Time Series Analysis Bayesian Methods Survival Analysis Causal Inference Glossary Index