Machine learning --also known as data mining or data analytics-- is
a fundamental part of data science. It is used by organizations in
a wide variety of arenas to turn raw data into actionable
information. Machine Learning for Business Analytics: Concepts,
Techniques, and Applications in R provides a comprehensive
introduction and an overview of this methodology. This best-selling
textbook covers both statistical and machine learning algorithms
for prediction, classification, visualization, dimension reduction,
rule mining, recommendations, clustering, text mining,
experimentation and network analytics. 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 R edition of Machine Learning for
Business Analytics. This edition also includes: - A new co-author,
Peter Gedeck, who brings over 20 years of experience in machine
learning using R - An expanded chapter focused on discussion of
deep learning techniques - 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 devoted to relevant case studies with
more than a dozen cases demonstrating applications for the machine
learning techniques - End-of-chapter exercises that help readers
gauge and expand their comprehension and competency of the material
presented - 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 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.
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