Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Machine learning is a fundamental part of data science and is used by organizations to turn raw data into actionable information. This fourth edition of Machine Learning for Business Analytics covers statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting, and network analytics. It also includes an expanded chapter on deep learning, a new chapter on experimental feedback techniques, a new chapter on responsible data science, updates and new material based on feedback from instructors, and a companion website with more than two dozen data sets and instructor materials.

Format: Hardback
Length: 624 pages
Publication date: 27 April 2023
Publisher: John Wiley & Sons Inc

Machine learning, also referred to as data mining or predictive 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.

Data Mining, a comprehensive textbook, offers an in-depth introduction and comprehensive overview of this methodology. The fourth edition of this bestselling textbook delves into both statistical and machine learning algorithms, enabling the prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting, and network analytics. Accompanied by hands-on exercises and real-life case studies, it also addresses managerial and ethical considerations for the responsible application of machine learning techniques.

In this fourth edition of Machine Learning for Business Analytics, readers will find several notable enhancements. An expanded chapter on deep learning explores the latest advancements in neural networks, offering a deeper understanding of this powerful technology. A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning, provides practical insights into optimizing machine learning models. Additionally, a dedicated chapter on responsible data science emphasizes the ethical implications and best practices associated with data science and machine learning.

To cater to the evolving needs of instructors and students, this textbook has been updated and revised based on feedback from educators 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 real-world applications of machine learning techniques, allowing readers to apply their knowledge in practical scenarios. End-of-chapter exercises further enhance comprehension and competency, while a companion website offers an abundance of data sets and instructor materials, including exercise solutions, slides, and case solutions.

As a comprehensive resource, Machine Learning for Business Analytics serves as an invaluable guide for professionals, researchers, and students seeking to leverage the power of machine learning in business analytics. Its comprehensive coverage, practical examples, and up-to-date material make it an essential tool for anyone looking to stay ahead in the rapidly evolving field of data science and business intelligence.

Weight: 1383g
Dimension: 254 x 185 x 33 (mm)
ISBN-13: 9781119829836
Edition number: 4 ed