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The only minor fault in my mind is that it could have been easier to explain some of the hairier concepts with mathematical formulas (which the author avoids, for legitimate reason) in an appendix. The fact that all examples are given in working code ensures that everything you need to start programming your own applications is provided for you. This is a minor issue, as all concepts can be supplemented with a simple web search. Excellent resource for beginners and experts alike. I was impressed with the organization and the concise explanations that nonetheless explained what you need to know to understand the methods in practical statistical programming being used today. An appendix is provided with seemingly similar purpose, but is underdeveloped.
Good depth, but still concise. This book is the opposite of A New Kind of Science. A fun, fast read. The code is well written, broadly applicable, and easy to modify.
Python is an excellent choice for this as it can be easy to read. This book is full of useful examples, showing you how to use "real" data, even how to get the "real" data. The downside: this book has over 1000 proposed errors and not 1 accepted errors on the O'reilly web site. Most O'reilly books are boring, useless documentation that you could find on the internet.
By not updating this book they are doing the community and they author a huge disservice. Check the O'reilly site to get the latest code updates. You can download the code examples but even those do not work 100%. That is a book that is somewhere in between pure theory, and pure practice. That observation and follow through is simply genius. The book is around 300 pages but it is very dense, if someone else wrote it, it would be 600 pages. For that reason if you are not fond of O'reilly books, don't worry this one is different.
Also the book was published in 2007 and the internet has changed since then so the API's are a little out of date. This book is spectacular, I love the way that the Author approaches a "new middle" ground of writing books. I had to study Python a little before I could totally digest the code. Some of the code simply does not work as it's written in the book.
This book seems to me like an excellent old school teacher among those ones who really take the right timing and words for carefully explaining you something probably difficult in an easy way so that you really will want to learn more about it. Examples are taken from the Web domain so this text can be very useful for people interested in combining BI and AI, among others. I am not completely finished reading it but I already think it's a great introductory book which is strongly committed with transmitting intuition and comprehension of its material (machine learning) usually hard for regular people. It focuses mainly on implementation and application but some general coverage of the underlying theory is done to motivate inexpert readers. Personally, I approve using Python, which is not (yet) my favorite language, I consider it actually as a plus because it complements very well author's intention of simplicity which is all over behind the book design.
Some complex topics are covered in a manner that anyone can understand. A must have for any software professional. This book provides good coverage of areas essential to modern web sites. I used this book to suppliment my college material and it helped me understand Gentic Algorithms, Path Finding and other algorithms by giving practicle examples of their use.
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