Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python - Hardcover

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Galit Shmueli, PhD, is Chair Professor at National Tsing Hua University’s Institute of Service Science, College of Technology Management. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan.

Peter C. Bruce is the Founder and former President of the Institute for Statistics Education at Statistics.com.

Peter Gedeck, PhD, is Senior Data Scientist at Collaborative Drug Discovery and Lecturer at the UVA School of Data Science. His speciality is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates.

Nitin R. Patel, PhD, is cofounder and lead researcher at Cytel Inc. He was also a co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a visiting professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

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Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.

This is the second Python edition of Machine Learning for Business Analytics. This edition also includes:

• A new chapter on generative AI (large language models or LLMs, and image generation)
• An expanded chapter on deep learning
• A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning
• A new chapter on responsible data science
• Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students
• A full chapter of cases demonstrating applications for the machine learning techniques
• End-of-chapter exercises with data
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions

This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.