Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R, Second Edition

Machine learning is a fundamental part of data science and is used by organizations to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and overview of this methodology, covering both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. It also discusses managerial and ethical issues for responsible use of machine learning techniques.

Format: Hardback
Length: 688 pages
Publication date: 08 February 2023
Publisher: John Wiley and Sons Ltd

Machine learning, also referred to as data mining or data analytics, holds a pivotal position within the realm of data science. It serves as a powerful tool utilized by organizations across diverse industries to transform raw data into valuable and actionable insights. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R offers a comprehensive introduction and comprehensive overview of this methodology. This widely acclaimed textbook delves into both statistical and machine learning algorithms, encompassing prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. In addition to hands-on exercises and real-life case studies, it addresses managerial and ethical considerations for the responsible deployment of machine learning techniques.

This is the second edition of Machine Learning for Business Analytics, and it boasts several notable enhancements. Firstly, we welcome a new co-author, Peter Gedeck, who brings a wealth of over 20 years of experience in machine learning utilizing R. Secondly, the chapter on deep learning techniques has been expanded to provide a comprehensive exploration of this cutting-edge field. Thirdly, a new chapter has been dedicated to experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning. Furthermore, a new chapter on responsible data science has been included, emphasizing the importance of ethical considerations in data science practices.

To cater to the evolving needs of instructors and students, this edition has undergone extensive updates and revisions based on feedback from educators teaching MBA, Masters in Business Analytics, and related programs, as well as from their students. A full chapter has been devoted to relevant case studies, showcasing more than a dozen real-world applications of machine learning techniques. End-of-chapter exercises have been designed to assist readers in assessing their comprehension and competency of the material presented. Additionally, a companion website offers an abundance of data sets and instructor materials, further enhancing the learning experience.

In summary, Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R serves as a comprehensive and authoritative resource for anyone seeking to harness the power of machine learning in business analytics. With its extensive coverage, practical examples, and up-to-date material, this textbook equips readers with the skills and knowledge necessary to effectively leverage machine learning techniques to drive innovation and achieve competitive advantage in their respective fields.

Weight: 1549g
Dimension: 254 x 178 x 34 (mm)
ISBN-13: 9781119835172