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Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition

4.3 out of 5 stars 22 customer reviews
ISBN-13: 978-1107422223
ISBN-10: 1107422221
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Editorial Reviews

Review

"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing Reviews

Book Description

Machine Learning brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, the book explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
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Product Details

  • Paperback: 409 pages
  • Publisher: Cambridge University Press; 1 edition (November 12, 2012)
  • Language: English
  • ISBN-10: 1107422221
  • ISBN-13: 978-1107422223
  • Product Dimensions: 7.4 x 0.7 x 9.7 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #51,820 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Scott C. Locklin VINE VOICE on January 23, 2013
Format: Paperback Vine Customer Review of Free Product ( What's this? )
I'm probably to be considered a "very advanced amateur" or "informal professional" in machine learning techniques. I never studied them in school, but I presently make a living in part by coding up new ones and coming up with ML solutions for commercial problems. Confronting the field for the first time, I wondered how people learned the stuff. Most of the introductory texts just covered neural nets ... and very very badly. Neural nets are still useful, and probably the most mature of machine learning techniques, but throwing them at a beginner without context is a recipe for confusion and dismay.

This text, by contrast, barely mentions them, and puts them in their proper context for the beginner. The right way to think about machine learning is starting with *very* basic statistical techniques and probability theory, and building up from there into simple classification and scoring systems, and then on to the rest of the field. The author of this text does it the right way.

One of the difficulties of didactic texts in the subject is ... machine learning is a very diverse field. All kinds of gizmos are helpful, and there isn't an obvious taxonomy, as there is in, say, linear time series models. The author takes a very high level view; breaking the field down into geometric, probabilistic and "logical" models. I believe this to be original, and a very powerful way of looking at things for the beginner.

The progression is well thought out, and each chapter comes with a useful summary and references (one of which has already proved helpful to me) for further reading.
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Format: Paperback Vine Customer Review of Free Product ( What's this? )
In my Advanced Statistics class one of the text books was The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. I found some of the chapters in this book heavy going and I had to read them several times and ask the professor lots of questions before I understood the material.

In contrast, Machine Learning by Peter Flach is a very well written, very gentle introduction to machine learning algorithms. Prof. Flach writes that he spent four years writing this book and it shows in the care with which the material is presented.

The mathematics used is algebra, exponents, summations, products and a bit of linear algebra. There are only a few places where derivatives are used (as it turns out, basic linear algebra can be used to describe many machine learning algorithms). The level of the Machine Learning makes it appropriate for an undergraduate Machine Learning course.

Machine Learning covers most of the core algorithms in machine learning. Of necessity what is provided is an overview of topics like linear regression and linear classifiers like Support Vector Machines. These are topics that are covered in depth in book like Applied Regression Analysis and An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.
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Format: Paperback
Over a decade ago, Peter Flach of Bristol University wrote a paper on the topic of "On the state of the art in machine learning: A personal review" in which he reviewed several, then recent books, related to developments in machine learning. This included Pat Langley’s Elements of Machine Learning (Morgan Kaufmann), Tom Mitchell’s Machine Learning (McGraw-Hill), and Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Ian Witten and Eibe Frank (Morgan Kaufman) among many others. Dr. Flach mentioned Michael Berry and Gordon Linoff’s Data Mining Techniques for Marketing, Sales, and Customer Support (John Wiley) for it's excellent writing style citing the paragraph below and commending "I wish that all computer science textbooks were written like this."

“People often find it hard to understand why the training set and test set are “tainted” once they have been used to build a model. An analogy may help: Imagine yourself back in the 5th grade. The class is taking a spelling test. Suppose that, at the end of the test period, the teacher asks you to estimate your own grade on the quiz by marking the words you got wrong. You will give yourself a very good grade, but your spelling will not improve. If, at the beginning of the period, you thought there should be an ‘e’ at the end of “tomato”, nothing will have happened to change your mind when you grade your paper. No new data has entered the system. You need a test set! Now, imagine that at the end of the test the teacher allows you to look at the papersof several neighbors before grading your own. If they all agree that “tomato” has no final ‘e’, you may decide to mark your own answer wrong. If the teacher gives the same quiz tomorrow, you will do better. But how much better?
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