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Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition
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Top Customer Reviews
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.Read more ›
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.Read more ›
“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?Read more ›
Most Recent Customer Reviews
There are better and cheaper books that provide better explaination in clearer and less confusing language than this one. Read morePublished 3 months ago by Pham The Phong
This looks like a very nice text, but the figures are badly done: in particular, items on them (scatter points, labels, ticks) are unacceptably small. Read morePublished 12 months ago by Alex F.
I don't think this book is well defined and structured. After reading so many rave-views, I realize that is is overrated. Read morePublished 15 months ago by Cico
What an amazing book, I got it about a month ago for a self-study routine and every page of this book has been a joy. Read morePublished 17 months ago by John
I bought this book to learn on my own but is very much written as a companion to a course. It is a very dense coverage of technical details of machine learning. Read morePublished 17 months ago by Amazon Customer