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Machine Learning for OpenCV

You're reading from   Machine Learning for OpenCV Intelligent image processing with Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781783980284
Length 382 pages
Edition 1st Edition
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Authors (2):
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Michael Beyeler Michael Beyeler
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Michael Beyeler
Michael Beyeler (USD) Michael Beyeler (USD)
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Michael Beyeler (USD)
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Toc

Table of Contents (13) Chapters Close

Preface 1. A Taste of Machine Learning 2. Working with Data in OpenCV and Python FREE CHAPTER 3. First Steps in Supervised Learning 4. Representing Data and Engineering Features 5. Using Decision Trees to Make a Medical Diagnosis 6. Detecting Pedestrians with Support Vector Machines 7. Implementing a Spam Filter with Bayesian Learning 8. Discovering Hidden Structures with Unsupervised Learning 9. Using Deep Learning to Classify Handwritten Digits 10. Combining Different Algorithms into an Ensemble 11. Selecting the Right Model with Hyperparameter Tuning 12. Wrapping Up

Representing categorical variables

One of the most common data types we might encounter while building a machine learning system are categorical features (also known as discrete features), such as the color of a fruit or the name of a company. The challenge with categorical features is that they don't change in a continuous way, which makes it hard to represent them with numbers. For example, a banana is either green or yellow, but not both. A product belongs either in the clothing department or in the books department, but rarely in both, and so on.

How would you go about representing such features?

For example, let's assume we are trying to encode a dataset consisting of a list of forefathers of machine learning and artificial intelligence:

In [1]: data = [
... {'name': 'Alan Turing', 'born': 1912, 'died': 1954},
... ...
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