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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

Introducing image processing


From the point of view of the computer, an image is a large rectangular array of pixel values. We wish to either process this image to generate a new or better image (perhaps with less noise, or with a different look). This is typically the area of image processing. We may also want to go from this array to a decision that is relevant to our application, which is better known as computer vision. Not everybody agrees with this distinction of the two fields, but its description is almost exactly how the terms are typically used.

The first step will be to load the image from the disk, where it is typically stored in an image-specific format such as PNG or JPEG, the former being a lossless compression format and the latter a lossy compression one that is optimized for subjective appreciation of photographs. Then, we may wish to perform preprocessing on the images (for example, normalizing them for illumination variations).

We will have a classification problem as a...

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