Download PDF Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

Download PDF Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

Download PDF Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

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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 DieThis 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 MultimediaThis 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

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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).

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Product details

Series: The MIT Press

Hardcover: 624 pages

Publisher: The MIT Press; 1 edition (July 24, 2015)

Language: English

ISBN-10: 0262029448

ISBN-13: 978-0262029445

Product Dimensions:

7 x 1.1 x 9 inches

Shipping Weight: 2.3 pounds (View shipping rates and policies)

Average Customer Review:

4.4 out of 5 stars

38 customer reviews

Amazon Best Sellers Rank:

#33,687 in Books (See Top 100 in Books)

Kindle version: images are too small.This is particularly bad for special chars and formulas which are rendered as images as they appear about as large as the punctuation.Normal diagrams are also small and must be viewed with the zoom function.Apologies for rating the book based on formatting, but there's no other apparent way to contact the publisher.Once the issues are resolved I will fix the rating to fix the "outlier" it has created.

Supervised machine learning only. Basically a bunch of applications for an undergrad CS class. Light on theory. Very well structured though and excellent if you want to see some applications of machine learning in action. For deeper treatment see coursera courses by Geoff Hinton of Toronto and the Stanford ML class.

I have already used machine algorithms in production with Spark and Python, but I wanted to have a better understanding of how the algorithms work and more importantly what the variations, strengths/weaknesses, and trade-offs are for each algorithm. This book was exactly what I've been looking for.The authors explain the algorithms fluidly without any reference to specific programming libraries or languages. They introduce the concepts very well before moving into the specifics of the logic and math behind the algorithms. Following a thorough explanation of how the algorithm works, the authors then describe variants and pitfalls based on their prior foundation.So, if you aren't a math major but would like to understand the concepts and details of how ML works along with practical knowledge of variants, parameter tuning, and trade-offs, then this book should be exactly what you need.Finally, the algorithms covered are the most commonly used in ML. AI isn't covered. Look at the Table of Contents to see which algorithms are explained.

Machine Learning is brilliantly explained in this outstanding book. You will learn the subject a lot better than in many other books in the market. The only downside of this book is the lack of examples with programming code, especially in Python. I strongly urge the authors to do so in a next edition. A lot in the area is learned by doing, by using good software development practices.

Great introductory book to this field. I would highly recommend this for computer scientists or other engineers looking to get an understanding of this field. I have read a number of books that are too heavy with theory and some that are a bit on the skimpy side and leave out details that are important for a true practical implementation. This has just the right mix.

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 languagesSome books combine theory and programmingThis 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 casesBut if you need mathematical background or programming background I think you need use another book.

Overall, the book is well written - plenty of examples and good approaches towards data preparation, analysis, and applied ML.

I wish I returned it. It did not have anything useful.

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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) PDF

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) PDF