Applied Statistical Learning
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Applied Statistical Learning
This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. It also explains how to construct n-gram variables from text data. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences; true to the book’s goal to facilitate the use of modern methods of data science in the field. Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science.
Applied Statistical Learning & Machine Intelligence: Mastering Core Concepts with Python, A Comprehensive Guide from Foundational Theory to Real-World Deployment
Master the Essential Toolkit for the Modern Data World Applied Statistical Learning & Machine Intelligence is your definitive guide to understanding and applying the cutting-edge statistical and machine learning techniques that are revolutionizing industries from finance and biotechnology to marketing and astrophysics. Designed for the Python era, this book bridges the critical gap between theoretical understanding and practical, deployable skill. Why This Book is Your Essential Resource: From Theory to Production: Unlike other textbooks that stop at theory, this book provides a complete learning journey. We cover everything from foundational probability and exploratory data analysis (EDA) to advanced neural networks and the crucial MLOps pipeline for model deployment. Hands-On Python-First Approach: Every key concept is paired with practical Python tutorials using essential libraries like pandas, scikit-learn, statsmodels, NumPy, Seaborn, XGBoost, and TensorFlow/PyTorch fundamentals. The labs are designed for both beginners and experienced programmers. Expanded, Future-Ready Content: While covering the core of statistical learning (linear regression, classification, resampling, tree-based methods, SVM, clustering), this book goes further. It includes dedicated sections on Deep Learning (CNNs, RNNs), Explainable AI (SHAP, LIME), handling imbalanced data, survival analysis, and ethical AI considerations. Built for Clarity and Application: Complex ideas are explained with intuitive explanations, full-color graphics, and real-world case studies. A dedicated chapter on model performance evaluation ties metrics directly to business context, ensuring your models are robust and actionable. The Complete End-to-End Project: The culmination of the book is a full-scale case study that walks you through a complete project—from framing the business problem and data acquisition to final model deployment via API. This is the major value-add that prepares you for the job market. What You Will Learn & Implement: Foundations: Data visualization, statistical refreshers, linear/logistic regression, and robust model evaluation. Core Machine Learning: Regularization (Ridge, Lasso), decision trees, ensemble methods (Random Forests, Gradient Boosting), unsupervised learning (PCA, t-SNE, clustering). Advanced Frameworks: Support Vector Machines, neural networks, CNNs for images, and RNNs for sequential data. Specialized Mastery: Techniques for interpretability (XAI), feature engineering, and working with unstructured text and image data. Deployment & Strategy: Essentials of MLOps, model deployment patterns, and navigating the trade-offs between bias, variance, and complexity. Who is This Book For? This book is ideal for university students in statistics, computer science, and data science; aspiring and practicing data scientists and analysts; and any professional or enthusiast who needs to intelligently apply statistical learning techniques using Python to extract meaningful insights from complex data. It is the perfect successor and companion for readers of the classic An Introduction to Statistical Learning with Applications in R (ISLR) who are now working in Python ecosystems. Gain the conceptual understanding and the practical expertise to build, interpret, and deploy intelligent models.
Applied Machine Learning
Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: Supervised learning Statistical learning Learning with support vector machines (SVM) Learning with neural networks (NN) Fuzzy inference systems Data clustering Data transformations Decision tree learning Business intelligence Data mining And much more