Machine Learning: A Concise Introduction, 2E by Steven W. Knox (.PDF)
File Size: 13.9 MB
Machine Learning: A Concise Introduction, 2nd Edition by Steven W. Knox
Requirements: .PDF reader, 13.9 MB | True PDF
Overview: New edition of a PROSE award finalist title on core concepts for machine learning, updated with the latest developments in the field, now with Python and R source code side-by-side. Machine Learning is a comprehensive text on the core concepts, approaches, and applications of machine learning. It presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. New content for this edition includes chapter expansions which provide further computational and algorithmic insights to improve reader understanding. This edition also revises several chapters to account for developments since the prior edition. In this book, the design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods, enabling readers to solve applied problems more efficiently and effectively. This book also includes methods for optimization, risk estimation, model selection, and dealing with biased data samples and software limitations ― essential elements of most applied projects. A volume in the popular Wiley Series in Probability and Statistics, Machine Learning offers the practical information needed for an understanding of the methods and application of Machine Learning for advanced undergraduate and beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields.
Genre: Non-Fiction > Tech & Devices
Free Download links: