The second edition of a comprehensive introduction to machine learning
approaches used in predictive data analytics, covering both theory and
practice.
Machine learning is often used to build predictive models by extracting
patterns from large datasets. These models are used in predictive data
analytics applications including price prediction, risk assessment,
predicting customer behavior, and document classification. This
introductory textbook offers a detailed and focused treatment of the
most important machine learning approaches used in predictive data
analytics, covering both theoretical concepts and practical
applications. Technical and mathematical material is augmented with
explanatory worked examples, and case studies illustrate the application
of these models in the broader business context. This second edition
covers recent developments in machine learning, especially in a new
chapter on deep learning, and two new chapters that go beyond predictive
analytics to cover unsupervised learning and reinforcement learning.
The book is accessible, offering nontechnical explanations of the ideas
underpinning each approach before introducing mathematical models and
algorithms. It is focused and deep, providing students with detailed
knowledge on core concepts, giving them a solid basis for exploring the
field on their own. Both early chapters and later case studies
illustrate how the process of learning predictive models fits into the
broader business context. The two case studies describe specific data
analytics projects through each phase of development, from formulating
the business problem to implementation of the analytics solution. The
book can be used as a textbook at the introductory level or as a
reference for professionals.
