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Fundamentals of Machine Learning for Predictive Data Analytics (Hardcover, 2nd Revised edition)
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Fundamentals of Machine Learning for Predictive Data Analytics (Hardcover, 2nd Revised edition)
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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.
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