Building Machine Learning Systems with Python
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. 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.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.
1115193558
Building Machine Learning Systems with Python
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. 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.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.
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Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

eBook

$32.99 

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Overview

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. 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.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.

Product Details

ISBN-13: 9781782161417
Publisher: Packt Publishing
Publication date: 07/26/2013
Sold by: Barnes & Noble
Format: eBook
Pages: 290
File size: 11 MB
Note: This product may take a few minutes to download.

About the Author

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 has over 10 years of experience in Machine Learning. He has a PhD from the School of Computer Science at Carnegie Mellon University, which is a very strong school in Machine Learning, and currently works in Computational Biology.

Table of Contents

  1. Getting Started with Python Machine Learning
  2. Learning How to Classify with Real-world Examples
  3. Clustering - Finding Related Posts
  4. Topic Modeling
  5. Classification - Detecting Poor Answers
  6. Classification II - Sentiment Analysis
  7. Regression - Recommendations
  8. Regression - Recommendations Improved
  9. Classification III - Music Genre Classification
  10. Computer Vision - Pattern Recognition
  11. Dimensionality Reduction
  12. Big(ger) Data
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