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Computer Science - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Description

Book Synopsis: This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

Details

Discover the power of data with The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. This comprehensive book from the esteemed Springer Series in Statistics is a must-have for anyone interested in unlocking the secrets hidden within data. Whether you're an aspiring data scientist or a seasoned professional, this book is your ultimate guide to making data-driven decisions and predictions. With its clear explanations, practical examples, and cutting-edge techniques, this second edition will revolutionize the way you approach data analysis.

Stay ahead of the curve in the fast-paced world of data mining with The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Developed by leading statisticians, this second edition combines the latest research with real-world applications, providing you with the most comprehensive and up-to-date knowledge in the field. From classification and regression to unsupervised learning and ensemble methods, this book covers it all. Elevate your skills and gain a competitive edge in data analysis with this invaluable resource.

Ignite your passion for data and unleash your potential with The Elements of Statistical Learning: Data Mining, Inference, and Prediction. This meticulously crafted second edition is a masterpiece that seamlessly bridges theory and practice. With its user-friendly approach and step-by-step explanations, you'll quickly grasp complex concepts and be able to apply them to real-life scenarios. Uncover patterns, extract insights, and make informed decisions with confidence. Don't miss out on this transformative journey - get your copy now!

Unlock the hidden treasures of data with The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Whether you're delving into the world of artificial intelligence or seeking to enhance your research methodologies, this book is an essential companion. Packed with powerful algorithms, advanced techniques, and practical tips, it equips you with the tools needed to extract valuable knowledge from vast datasets. Embrace the future of data analysis and embark on a dynamic learning experience today!

Ready to take your data analysis skills to new heights? Look no further than The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. This best-selling tome, widely regarded as the "bible" of statistical learning, provides a comprehensive foundation for mastering the art and science of data mining. With its deep insights, rich examples, and thorough explanations, you'll gain the expertise to uncover hidden patterns, make accurate predictions, and optimize business strategies. Don't miss out on this transformative journey - secure your copy now!

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