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Building Machine Learning Systems with Python (Community Experience Distilled) [Englisch] [Taschenbuch]

Willi Richert , Luis Pedro Coelho

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Kurzbeschreibung

26. Juli 2013 Community Experience Distilled

Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

Overview

  • Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems.
  • Covers classification, regression, feature engineering, and much more guided by practical examples.
  • A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects.

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.

What you will learn from this book

  • Build a classification system that can be applied to text, images, or sounds
  • Use scikit-learn, a Python open-source library for machine learning
  • Explore the mahotas library for image processing and computer vision
  • Build a topic model of the whole of Wikipedia
  • Get to grips with recommendations using the basket analysis
  • Use the Jug package for data analysis
  • Employ Amazon Web Services to run analyses on the cloud
  • Recommend products to users based on past purchases

Approach

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.


Wird oft zusammen gekauft

Building Machine Learning Systems with Python (Community Experience Distilled) + Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Preis für beide: EUR 66,67

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Produktinformation


Produktbeschreibungen

Über den Autor und weitere Mitwirkende

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..


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Amazon.com: 4.1 von 5 Sternen  24 Rezensionen
48 von 51 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Quite Frustrating... but a helpful author. 6. Oktober 2013
Von Brian Du Preez - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
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 :)
19 von 20 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen What's the target audience for this? 19. November 2013
Von Andrew Diamond - Veröffentlicht auf Amazon.com
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
19 von 23 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Manish Bhoge 30. September 2013
Von Manish Bhoge - Veröffentlicht auf Amazon.com
Format:Taschenbuch
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 von 8 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Too many typos! 21. März 2014
Von not really - Veröffentlicht auf Amazon.com
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.
7 von 7 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Too Many Bugs in Code 20. März 2014
Von Schwallie - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
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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