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… liberally illustrated with many programming examples, using Python. It includes a basic primer on Python and has an accompanying website.
It has excellent breadth, and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of concepts and theory. …
I think the author has succeeded in his aim: the book provides an accessible introduction to machine learning. It would be excellent as a first exposure to the subject, and would put the various ideas in context …
This book also includes the first occurrence I have seen in print of a reference to a zettabyte of data (1021 bytes) — a reference to "all the world’s computers" being estimated to contain almost a zettabyte by 2010.
—David J. Hand, International Statistical Review (2010), 78
If you are interested in learning enough AI to understand the sort of new techniques being introduced into Web 2 applications, then this is a good place to start. … it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on … . This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.
—I-Programmer, November 2009
Product Description
Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.
Theory Backed up by Practical Examples
The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.
Highlights a Range of Disciplines and Applications
Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.
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Most helpful customer reviews
1 of 1 people found the following review helpful
2.0 out of 5 stars
Not great.,
By
This review is from: Machine Learning: An Algorithmic Perspective (Hardcover)
For the price, it's not worth it. There's not much in this book. Details are glossed over. Not much intuition is provided. I suggest Hastie, Tibshirani and Friedman...
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
3.9 out of 5 stars (19 customer reviews) 74 of 78 people found the following review helpful
5.0 out of 5 stars
A great book for students,
By John - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Machine Learning: An Algorithmic Perspective (Hardcover)
This is an good book on machine learning for students at the advancedundergraduate or Masters level, or for self study, particularly if some of the background math (eigenvectors, probability theory, etc) is not already second nature. Although I am now familiar with much of the math in this area and consider myself to have intermediate knowledge of machine learning, I can still recall my first attempts to learn some mathematical topics. At that time my approach was to implement the ideas as computer programs and plot the results. This book takes exactly that approach, with each topic being presented both mathematically and in Python code using the new Numpy and Scipy libraries. Numpy resembles Matlab and is sufficiently high level that the book code examples read like pseudocode. (Another thing I recall when I was first learning was the mistaken belief that books are free from mistakes. I've since learned to expect that every first edition is going to have some, and doubly so for books with math and code examples. However the fact that many of the examples in this book produce plots is reassuring.) As mentioned I have only intermediate knowledge of machine learning, and have no experience with some techniques. I learned regression trees and ensemble learning from this book -- and then implemented an ensemble tree classifier that has been quite successful at our company. Some other strong books are the two Bishop books (Neural Networks for Pattern Recognition; Pattern Recognition and Machine Learning), Friedman/Hastie/Tibshirani (Elements of Statistical Learning) and Duda/Hart/Stork (Pattern Classification). Of these, I think the first Bishop book is the only other text suitable for a beginner, but it doesn't have the explanation-by-programming approach and is also now a bit dated (Marsland includes modern topics such as manifold learning, ensemble learning, and a bit of graphical models). Friedman et al. is a good collection of algorithms, including ones that are not presented in Marsland; it is a bit dry however. The new Bishop is probably the deepest and best current text, but it is probably most suited for PhD students. Duda et al would be a good book at a Masters level though its coverage of modern techniques is more limited. Of course these are just my impressions. Machine learning is a broad subject and anyone using these algorithms will eventually want to refer to several of these books. For example, the first Bishop covers the normalized flavor of radial basis functions (a favorite technique for me), and each of the mentioned books has their own strengths. 18 of 18 people found the following review helpful
3.0 out of 5 stars
A decent idea, but very flawed in the execution.,
By Deon Garrett - Published on Amazon.com
This review is from: Machine Learning: An Algorithmic Perspective (Hardcover)
Modern Machine Learning is deeply statistical and mathematical in nature, and as others have said, this book aims to trade off some rigor in favor of a more intuitive approach. That in itself is not a bad idea; there should be room for a book that gives the reader a working knowledge of the more important techniques, even if they don't necessarily understand how they work at a deep level. Unfortunately, this book stumbles quite badly in many respects.We chose the book for an introductory course in Machine Learning at my university, as our students often don't have the level of mathematical background necessary for treatments like the Bishop book. However, I and my colleague often had to resort to essentially replacing entire chapters of material due to serious flaws in the text. In some cases, the author gives definitions for commonly available terms that are simply factually incorrect. For example, the chapter on Evolutionary Algorithms repeatedly confuses parent selection and elitism, states that crossover cannot be defined for non-binary representations, and other similar mistakes. In the chapter on Reinforcement Learning, most of the material is at least correct, but does not progress in any meaningful manner. For instance, the author introduces TD(') methods using execution traces, gives the formula for updating the trace, and then simply stops. There is no indication of what you should do with this value once it's been calculated. There are quite a number of these sort of issues in the book. I would also concur with the other reviewers who felt that the idea of stripping away unnecessary mathematical formalism has simply been taken too far here. It sits in an awkward middle ground: it doesn't provide enough formalism to tell you how the algorithms really work, but it simultaneously wastes so much space in trying to hand-wave its way around the issue that the code is relegated to fragments that don't give a very complete picture. I think the book would be more successful if it either accepted that at least some mathematical sophistication is necessary and dispensed with some of the cutesy hand-holding, or else dispensed with the pretense of explaining the material in an academically interesting way and simply focused on implementations of the algorithms along with explanations of the code. On the whole, I think the book does have some value. It's one of the few books covering modern topics in machine learning that can be used by someone without a pretty solid grounding in statistics, and if the reader is motivated to augment it heavily with other sources, it can be a decent starting point for looking into some technique. But that's about it really. I think it's flawed enough that as a single source of information, it's likely to not meet its intended purpose with a great deal of success. 17 of 17 people found the following review helpful
5.0 out of 5 stars
Different than other 'textbooks' on ML,
By Doug Y'barbo - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Machine Learning: An Algorithmic Perspective (Hardcover)
I first saw this book on a colleague's bookshelf; i picked it up and briefly looked through it. The simple diagrams and the relative lack of equations (compared to e.g., Bishop) might suggest to you that it's a 'beginner' text--and by that i mean that the textbook is only an introduction to ML and doesn't teach you enough so that you can begin writing ML code to solve real classification/regression problems. That's what i though at first, and i was wrong. This is an introductory text, but only in the sense that it's accessible to more or less anyone, but this book's explanation/theory and the practical examples (in python) are brilliantly integrated--the explanation (often summarizing two or three pages of terse equations found in other textbooks, in a single paragraph) helped me grok the code, and the code reinforced the theory behind the algorithm.I don't think there's another ML book like this--it's aimed right at the blind spot framed by applied math reference-type books such as Bishop on one end, and books like 'Programming Collective Intelligence' which are dense with working ML code, but light on theory. I also like this book because the code is written in NumPy, rather than in the Python standard library code. NumPy is what you would use 'in the real world' to code an ML algorithm, and if you understand the matrix-driven syntax, then the code is far more concise (e.g., no triply nested recursive loops) than the same algorithms coded using just the Python standard library. In sum, an excellent book. |
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