Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.

Publication date: 26 Jul 2013

ISBN-10: 1782161406

ISBN-13: 9781782161400

Paperback: 290 pages

Views: 3,010

Type: Book

Publisher: Packt Publishing

License: n/a

Post time: 06 Mar 2017 10:30:00

Building Machine Learning Systems with Python

Building Machine Learning Systems with Python This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
Tag(s): Machine Learning Python
Publication date: 26 Jul 2013
ISBN-10: 1782161406
ISBN-13: 9781782161400
Paperback: 290 pages
Views: 3,010
Document Type: Book
Publisher: Packt Publishing
License: n/a
Post time: 06 Mar 2017 10:30:00
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From the Book Description:
Coelho and Richert wrote: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.




About The Author(s)


Luis Pedro Coelho is a computational biologist. He works at the European Molecular Biology Laboratory (EMBL) in Peer Bork's group in microbial community analysis using metagenomics. He has a PhD from Carnegie Mellon University (2011), where he worked on bioimage informatics for subcellular location analysis with Bob Murphy. He is interested in combining meta'omics (methods that obtain high-throughput information on microbial communities such as metagenomics and metatranscriptomics) analysis with machine learning approaches to learn about microbial communities in different environments.

Luis Pedro Coelho

Luis Pedro Coelho is a computational biologist. He works at the European Molecular Biology Laboratory (EMBL) in Peer Bork's group in microbial community analysis using metagenomics. He has a PhD from Carnegie Mellon University (2011), where he worked on bioimage informatics for subcellular location analysis with Bob Murphy. He is interested in combining meta'omics (methods that obtain high-throughput information on microbial communities such as metagenomics and metatranscriptomics) analysis with machine learning approaches to learn about microbial communities in different environments.


Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees.

Willi Richert

Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees.


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