Erudite yet real-world relevant. It's true that predictive analytics andmachine learning go hand-in-hand: To put it loosely, prediction depends on learningfrom past examples. And, while Fundamentals succeeds as acomprehensive university textbook covering exactly how that works, the authors alsorecognize that predictive analytics is today's most booming commercial applicationof machine learning. So, in an unusual turn, this highly enriching opus brings theconcepts to light with industry case studies and best practices, ensuring you'llexperience the real-world value and avoid getting lost in abstraction.Eric Siegel, Ph.D., founder of Predictive Analytics World; author ofPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, orDieThis book provides excellent descriptions of the key methods used inpredictive analytics. However, the unique value of this book is the insight itprovides into the practical applications of these methods. The case studies and thesections on data preparation and data quality reflect the real-world challenges inthe effective use of predictive analytics.PA¡draig Cunningham, Professor of Knowledge and Data Engineering, School ofComputer Science, University College Dublin; coeditor of Machine LearningTechniques for MultimediaThis is a wonderful self-contained book that touches upon the essentialaspects of machine learning and presents them in a clear and intuitive light. Withits incremental discussions ranging from anecdotal accounts underlying the 'bigidea' to more complex information theoretic, probabilistic, statistic, andoptimization theoretic concepts, its emphasis on how to turn a business problem intoan analytics solution, and its pertinent case studies and illustrations, this bookmakes for an easy and compelling read, which I recommend greatly to anyoneinterested in finding out more about machine learning and its applications topredictive analytics.Nathalie Japkowicz, Professor of Computer Science, University of Ottawa; coauthorof Evaluating Learning Algorithms: A ClassificationPerspective
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
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.