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scikit-learn Cookbook , Second Edition

You're reading from   scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
Author Profile Icon Julian Avila
Julian Avila
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Table of Contents (13) Chapters Close

Preface 1. High-Performance Machine Learning – NumPy FREE CHAPTER 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Quantizing an image with k-means clustering

Image processing is an important topic in which clustering has some application. It's worth pointing out that there are several very good image processing libraries in Python. scikit-image is a sister project of scikit-learn. It's worth taking a look at if you want to do anything complicated.

A big point of this chapter is that images are data as well and clustering can be used to try to guess where some objects in an image are. Clustering can be part of an image processing pipeline.

Getting ready

We will have some fun in this recipe. The goal is to use a cluster to blur an image. First, we'll make use of SciPy to read the image. The image is translated in a three...

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