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Building Machine Learning Systems with Python [Kindle Edition]

Willi Richert , Luis Pedro Coelho
4.0 out of 5 stars  See all reviews (2 customer reviews)

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Product Description

Product Description

In Detail

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.

Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.

Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.

Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.


A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have learnt critical aspects of machine learning Python projects and experienced the power of ML-based systems by actually working on them.

Who this book is for

This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.

This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

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..

Product details

  • Format: Kindle Edition
  • File Size: 4944 KB
  • Print Length: 292 pages
  • Page Numbers Source ISBN: 1782161406
  • Publisher: Packt Publishing (26 July 2013)
  • Sold by: Amazon Media EU S.ą r.l.
  • Language: English
  • ISBN-10: 1782161414
  • ISBN-13: 978-1782161417
  • ASIN: B00E7NC9D2
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #121,489 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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6 of 6 people found the following review helpful
3.0 out of 5 stars Good introductory text into Machine Learning 14 Oct. 2013
For someone new to ML, this book is a very good start. It presents some theory just enough to get engaged, it contains source code, diagrams and technicalities on how to work with the packages, how to tweak the parameters and how to improve the results.

In addition to this, it covers a quite broad range of topics in the field of Machine Learning, from the most basic classifications to form recognition (sound and image).

There are downsides to the book, though:
1) it sometimes lacks the much needed underlying theory (there are exceptions, such as the mini-presentation on Bayesian calculus). Imagine one trying to teach discrete probabilities just by throwing dice (experiment) without teaching combinatorics first (theory).
2) the examples rely too much on external libraries and Python packages. There is not a single example on how those library functions might be implemented - not even a rough sketch.
3) it switches gears between subjects quite often, without bringing them to completion. Example: learning from an incomplete set of features (super exciting topic) abruptly gives way to ... recommendation and prediction rating - within the the same sub-chapter.

Overall, this book is a good introductory text on the subject. It has an alert, engaging style, it covers a broad spectrum of subjects in the field and it contains references to good extra resources for those who want to learn more.
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Quite an impressive book. I’m not sure what has amazed me most: the examples, so real and complex but still, easy to follow, or the easiness through which the authors introduce the reader to many different machine learning techniques without throwing at him scaring math formulas. Certainly both things. And not only those.

As clearly stated in the preface, this book it’s not about scikit-learn. It does not expect the reader to master it. Similarly, it does not aim to teach him the library from install to Z. The book is about Python and machine learning. Many examples are, indeed, pure Python.

Still, scikit-learn is used. Sometimes alone, often in combination with other widely known libraries, such as Numpy, Scipy, nltk and matplotlib. While scikit-learn and nltk are not taken from granted, whoever is holding the book on his hands is expected to have been exposed already to both the numerical libraries and to matplotlib. Methods such as norm() and rvs() are not explained and if you are not familiar with them, get ready to open both the reference and a terminal with the Python interpret.

Not only do the authors rely on those must know libraries. They also show the readers some less known gem, such as gensim (a real beauty that doesn’t ship with scikit-learn).

As I was saying at the beginning, the examples are very interesting. They are real world challenges, clearly explained step by step with many accompanying charts and schemas. The source code that comes with the book has some snippet that you really wanna copy somewhere safe.

I have particularly enjoyed the idea behind the book itself: introduce the reader to machine learning without exposing him to the mathematical details behind it.
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Most Helpful Customer Reviews on (beta) 4.2 out of 5 stars  26 reviews
56 of 59 people found the following review helpful
3.0 out of 5 stars Quite Frustrating... but a helpful author. 6 Oct. 2013
By Brian Du Preez - Published on
Format:Kindle Edition|Verified Purchase
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.

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.
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 :)
24 of 25 people found the following review helpful
2.0 out of 5 stars What's the target audience for this? 19 Nov. 2013
By Andrew Diamond - Published on
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
11 of 11 people found the following review helpful
2.0 out of 5 stars Too many typos! 21 Mar. 2014
By Amazon Customer - Published on
Format:Kindle Edition
I like the approach and level of this book, but I don't want to waste time figuring out whether a bug in the code is mine or the authors'. This book might be useful after it has undergone some serious proofing.
21 of 25 people found the following review helpful
5.0 out of 5 stars Manish Bhoge 30 Sept. 2013
By Manish Bhoge - Published on
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? how to implement TF-IDF for meaningful text comparison? etc.
5. I had big time difficulties in understanding correlation and regression. but explanations of supported SCIKIT libraries made it pretty simple.
6. I really enjoyed the chapter for implementation of text (post) data comparison and clustering.
7. Big data analysis using JUG came as surprise to me when i was about to complete the reading. It is really interesting to compare this topic with Map reduce implementation. My work is still in progress on this...
8. overall this book covers almost everything that PYTHON can cover for you in data analysis and machine learning.

1. You should know ML concepts in advance. This is not the book to start ML learning. Obviously, It's already proclaim that it is programming ML in python.
2. Don't expect in details explanation of any algorithm. Like, when it says "TF-IDF" it just explain in a paragraph what TF-IDF is. And, it implementation in Scikit-learn libraries.
3. You mush have moderate level of understanding on python. If you are not at all familiar with PYTHON then spend some time on python primitive data types and programmability before you start this book.
4..... yeah that's it. I don't have any more points to mention as negative.

Overall, very good book when you have some knowledge on ML (and its algorithms) and Python. But you don't know how to implement these concept in data analysis. Then this is the book. Go get it!
8 of 8 people found the following review helpful
2.0 out of 5 stars Too Many Bugs in Code 20 Mar. 2014
By Schwallie - Published on
Format:Kindle Edition|Verified Purchase
Unfortunately this book is basically a non-starter. I can't seem to get through any of it as the code doesn't work in most cases. I agree with a previous reviewer -- I could spend my time debugging and making it work, but that's not why I bought the book. I shouldn't have to jump through hurdles to read this.

I wish I could get a refund.
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