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Product Details

  • Paperback: 290 pages
  • Publisher: Packt Publishing (July 26, 2013)
  • Language: English
  • ISBN-10: 1782161406
  • ISBN-13: 978-1782161400
  • Product Dimensions: 9.2 x 7.5 x 0.6 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #179,912 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

Willi Richert

Willi Richert has a PhD in Machine Learning/Robotics and currently works for Microsoft in the Bing Core Relevance Team. He performs statistical machine translation.



Luis Pedro Coelho

Luis Pedro Coelho is a Computational Biologist: someone who uses computers as a tool to understand biological systems. Within this large field, Luis works in Bioimage Informatics, which is the application of machine learning techniques to the analysis of images of biological specimens. His main focus is on the processing of large scale image data. With robotic microscopes, it is possible to acquire hundreds of thousands of images in a day, and visual inspection of all the images becomes impossible. Luis has a PhD from Carnegie Mellon University, which is one of the leading universities in the world in the area of machine learning. He is also the author of several scientific publications. Luis started developing open source software in 1998 as a way to apply to real code what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on mahotas, the popular computer vision package for Python, and is the contributor of several machine learning codes..

Customer Reviews

4.1 out of 5 stars
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See all 19 customer reviews
Some of topics are explained very well.
dgg32
Python has an excellent ecosystem of libraries for Machine Learning.
Sujit Pal
1. You should know ML concepts in advance.
Manish Bhoge

Most Helpful Customer Reviews

40 of 43 people found the following review helpful By Brian Du Preez on October 6, 2013
Format: Kindle Edition Verified Purchase
Edit:
Willi Richert, has been quite helpful and has looked at the issues I was having and resolved some of them, so especially if you are working on Windows, make sure you get the code from GitHub.
I have not returned to complete working through the rest book as yet, will as soon as I have time.

Original:
To be completely honest I had great hope for this book, it was theoretically exactly what I was looking for, a practical guide to getting up and running with Machine Learning and some of it major Python packages.
But...
From chapter 3, there were code discrepancies between what was in the book, what was supplied and then eventually what I got working...
I am not going to bother going into all the errors / issues, the 2 major ones that made me "shelve" the book and start looking for new study material:
1. After the 9GB download for chapter 5, the supplied source doesn't work and contains requirements to 32bit libs... huge waste of time...
2. After moving onto in chapter 6, and after 24 hours of downloading tweets for sentiment analysis... I checked the files and they only contained "The Twitter REST API v1 is no longer active. Please migrate to API v1.1".

Yes, I could go debug and fix the code / errors in other peoples code... but that is not how I want to spend my time learning a new subject, I have enough of that in my day job as a software developer :)
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17 of 19 people found the following review helpful By Manish Bhoge on September 30, 2013
Format: Paperback
Machine learning is an intricate philosophy and it involves lot of mathematical complexities to bring it into a practice of data analysis. This book simply eradicate those intricacies of programming and implementation of machine learning algorithms. In all, it makes machine learning code pretty simple. Understanding "WHAT" is machine learning is not the purpose of this book. However, this book is designed around the concept "HOW" to implement machine learning algorithms. I would like to add here that it is not only explain you "HOW" to program the algorithms but it also helps you to think "HOW BEST" we can program it. Let me start with some + and few - of the books. But before that remember, as title clarifies, this book is all around (hovers around) Python implementation of machine learning i.e. SCIKIT-LEARN libraries, Scipy and NUMPy. That's the boundary.

+
1. Very clear and precise declaration from Author that this book is more about implementation of ML than Concept.
2. It starts with teaching very basic of data analysis of preprocessing and cleaning up the data along with implementation of Array, indexes, Vector and Matrices using python libraries. This helps reader to make aware about WHAT basics they should build before getting into more complex problems of machine learning. I really liked the "tiny" machine learning program. It's like writing "Hello Word" in any other programming book.
3. Beauty is that it takes you slowly into the implementation of classification problem, Text data processing, Clustering, Regression and sentiment analysis.
4. Though the breadth of topics is vast but it touches every small corner of related topic. For example: When explaining text comparison method it explains how STOP WORD can be done?
Read more ›
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7 of 7 people found the following review helpful By Andrew Diamond on November 18, 2013
Format: Kindle Edition
I haven't gone through the entire book yet but so far it seems to have little to recommend it. There's two possible audiences for a book like this

1) A Python programmer who wants to do Machine learning
2) A person with expertise in Machine Learning who wants to learn how to do it in Python.

This book addresses neither of these audiences. If you're a random Python programmer, 1) above, this is really a terrible way to learn machine learning. The internet is filled with tutorials that are infinitely more thorough, better, and easier to understand than this. If your the second type, like me, 2) the Machine learning guy who wants to know how to do it in Python, this book will drive you batty. It's code, so far, is largely undocumented and doesn't match the book and the book doesn't explain the Python well enough.

You might think that writing a book like this would always be impossible by I suggest you look at Data Mining with R: Learning with Case Studies (1439810184 I'd kill for that book in Python
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7 of 7 people found the following review helpful By Sujit Pal on October 21, 2013
Format: Paperback
Python has an excellent ecosystem of libraries for Machine Learning. The libraries are all well-documented but sometimes it is hard to figure out how to solve a problem end-to-end using one or more of these libraries. This book attempts to fill that niche. It contains 12 chapters, each focusing on one or two ML problems, and shows how an expert ML practitioner would build and evaluate solutions for these problems. The main focus of the book is on the famous Scikits-Learn library, along with its dependencies Numpy and Scipy, but there is also coverage of gensim (for topic modeling), mahotas (for image processing), jug and starcluster (for distributed computing). The tone of the book is very practical and hands-on, in the rare cases where theory is explained, it is done without math. At the same time, the book is much more than just an introduction to Python ML libraries - you will come away learning "insider secrets" that you can do to improve your solution and which are already available as API calls within one of these libraries.

The authors say that this book was written to their younger selves - in my opinion a very accurate representation of the target audience. I believe the people who would benefit most from the book are those who are programming using the Python/sklearn ecosystem already at competitions or at work but who are still not at the top of their game. This book can help you get (at least part of the way) there.
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