摘要
"This book provides statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, time series forecasting, text mining and network analysis. The new edition has been updated with new material including automated machine learning (AutoML). Comprised of 24 chapters, this edition has two new chapters; the first covers generative AI, with a detailed introduction providing its foundations, its business uses, and a discussion on its data requirements, including end of chapter problems. The second new chapter presents responsible data science, supported with its principles, legal considerations, and detailed examples with end of chapter problems for students. Building from the previous edition, this book is an ideal textbook for graduate and upper-undergraduate level courses in data science, predictive analytics, and business analytics and an excellent reference for analysts, researchers, and data science practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology"-- Provided by publisher.
Includes bibliographical references and index.