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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

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787286382

Length 374 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Trent Hauck

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

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-dimensional array; the x and y coordinates describe the height and width, and the third dimension represents the RGB values for each image.

Begin by downloading or moving an .jpg image to the folder where your IPython notebook is located. You can use a picture of yours. I use a picture of...

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Authors (1)

Trent Hauck

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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