Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner

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 RapidMiner provides a comprehensive introduction and overview of this methodology, with hands-on exercises and real-life case studies. It is an ideal resource for upper-level undergraduate and graduate-level courses in data science, predictive analytics, and business analytics, as well as for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

Format: Hardback
Length: 736 pages
Publication date: 20 March 2023
Publisher: John Wiley & Sons Inc

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 insights. This comprehensive textbook, Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner, offers a thorough introduction and comprehensive overview of this methodology. Authored by renowned experts, this best-selling publication delves into both statistical and machine learning algorithms, enabling the prediction, classification, visualization, dimension reduction, rule mining, recommendation systems, clustering, text mining, experimentation, and network analytics. In addition to providing hands-on exercises and real-life case studies, it addresses managerial and ethical considerations for the responsible application of machine learning techniques.

This seventh edition of Machine Learning for Business Analytics marks a significant milestone, as it is the first to employ the RapidMiner software. Moreover, this edition boasts the inclusion of a new co-author, Amit Deokar, who brings extensive experience in teaching business analytics courses utilizing RapidMiner. The book also integrates the use of RapidMiner, an open-source machine learning platform that has gained widespread popularity in recent years. Furthermore, an expanded chapter dedicated to discussing deep learning techniques has been incorporated, along with a new chapter on experimental feedback techniques encompassing A/B testing, uplift modeling, and reinforcement learning. Additionally, a new chapter on responsible data science has been added, along with updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics, and related programs, as well as from their students. A full chapter devoted to relevant case studies showcases more than a dozen cases demonstrating the practical applications of machine learning techniques.

To enhance the learning experience, the book concludes with comprehensive end-of-chapter exercises. Overall, Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner serves as an essential resource for professionals, researchers, and students seeking to harness the power of machine learning in business analytics and gain a competitive edge in today's data-driven world.

Weight: 1364g
Dimension: 183 x 262 x 35 (mm)
ISBN-13: 9781119828792