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Fundamentals of Machine Learning for Predictive Data Analytics


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Erudite yet real-world relevant. It's true that predictive analytics and machine learning go hand-in-hand: To put it loosely, prediction depends on learning from past examples. And, while Fundamentals succeeds as a comprehensive university textbook covering exactly how that works, the authors also recognize that predictive analytics is today's most booming commercial application of machine learning. So, in an unusual turn, this highly enriching opus brings the concepts to light with industry case studies and best practices, ensuring you'll experience the real-world value and avoid getting lost in abstraction. -- Eric Siegel, Ph.D., founder of Predictive Analytics World; author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die This book provides excellent descriptions of the key methods used in predictive analytics. However, the unique value of this book is the insight it provides into the practical applications of these methods. The case studies and the sections on data preparation and data quality reflect the real-world challenges in the effective use of predictive analytics. -- Padraig Cunningham, Professor of Knowledge and Data Engineering, School of Computer Science, University College Dublin; coeditor of Machine Learning Techniques for Multimedia This is a wonderful self-contained book that touches upon the essential aspects of machine learning and presents them in a clear and intuitive light. With its incremental discussions ranging from anecdotal accounts underlying the 'big idea' to more complex information theoretic, probabilistic, statistic, and optimization theoretic concepts, its emphasis on how to turn a business problem into an analytics solution, and its pertinent case studies and illustrations, this book makes for an easy and compelling read, which I recommend greatly to anyone interested in finding out more about machine learning and its applications to predictive analytics. -- Nathalie Japkowicz, Professor of Computer Science, University of Ottawa; coauthor of Evaluating Learning Algorithms: A Classification Perspective

About the Author

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is the coauthor of Data Science (also in the MIT Press Essential Knowledge series) and Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press). Brian Mac Namee is a Lecturer at University College Dublin. Aoife D'Arcy is CEO of The Analytics Store, a data analytics consultancy and training company.

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