Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies Book PDF, ePub eBook

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Book Descriptions:

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.

After discussing the trajectory from
data to insight to decision, the book describes four approaches to machine learning:
information-based learning, similarity-based learning, probability-based learning,
and error-based learning. Each of these approaches is introduced by a nontechnical
explanation of the underlying concept, followed by mathematical models and
algorithms illustrated by detailed worked examples. Finally, the book considers
techniques for evaluating prediction models and offers two case studies that
describe specific data analytics projects through each phase of development, from
formulating the business problem to implementation of the analytics solution. The
book, informed by the authors' many years of teaching machine learning, and working
on predictive data analytics projects, is suitable for use by undergraduates in
computer science, engineering, mathematics, or statistics; by graduate students in
disciplines with applications for predictive data analytics; and as a reference for
professionals.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

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