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Ebook Download Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press), by John D. Kell
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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.
- Sales Rank: #25885 in Books
- Published on: 2015-07-24
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .88" w x 7.00" l, .0 pounds
- Binding: Hardcover
- 624 pages
Review
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.
(Pádraig 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 a Lecturer at the Dublin Institute of Technology, and a founding member of DIT's Applied Intelligence Research Center. 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.
Most helpful customer reviews
16 of 17 people found the following review helpful.
A future Classic. This book rigorously and clearly defines ...
By bbread
A future Classic. This book rigorously and clearly defines the key terms in Machine Learning. You will also find explanations of the core concepts of machine learning algorithms and enough math and images to consolidate your understanding. I encourage people to read this book before reading "An Introduction to Statistical Learning". Highly recommended
16 of 18 people found the following review helpful.
best book for practioner and not good book for programming or math background
By I. Kleiner
I am ML specialist and instructor.
There are many different types of books in Machine Learning. That cover various aspects of the field.
Some books are base on theoretic side: Learning from the Data.
Some books provide a gentle way for programming for Machine Learning in different languages
Some books combine theory and programming
This book "Fundamentals of Machine Learning" a good written book for practitioner in machine learning. For people that want to know how machine learning experts work. That processes they use, and how them organize there work.
In additional basic properties and ideas of general algorithms discussed.
This book uses excellent plant English, many examples and real cases
But if you need mathematical background or programming background I think you need use another book.
15 of 18 people found the following review helpful.
Much needed book for practioners
By LanternRouge
This book will teach you CRISP-DM workflow and how to think about analytics in a professional manner in addition to the core ML algorithms. The authors cover crucial practical information and work habits every data scientist should know. I do not know of any way to get this information other than making a lot of mistakes in the field. Well done! I encourage all my students to pick up a copy.
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