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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python 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.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 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 Where to Learn More about Machine Learning Index

Chapter 12. Big(ger) Data

While computers keep getting faster and have more memory, the size of the data has grown as well. In fact, data has grown faster than computational speed, and this means that it has grown faster than our ability to process it.

It is not easy to say what is big data and what is not, so we will adopt an operational definition: when data is so large that it becomes too cumbersome to work with, we refer to it as big data. In some areas, this might mean petabytes of data or trillions of transactions; data that will not fit into a single hard drive. In other cases, it may be one hundred times smaller, but just difficult to work with.

We will first build upon some of the experience of the previous chapters and work with what we can call the medium data setting (not quite big data, but not small either). For this we will use a package called jug, which allows us to do the following:

  • Break up your pipeline into tasks

  • Cache (memoize) intermediate results

  • Make use of multiple cores...

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