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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
Author Profile Icon Michael Beyeler
Michael Beyeler
Michael Beyeler (USD) Michael Beyeler (USD)
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Michael Beyeler (USD)
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Table of Contents (13) Chapters Close

Preface 1. A Taste of Machine Learning FREE CHAPTER 2. Working with Data in OpenCV and Python 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

Using classification models to predict class labels

With these tools in hand, we can now take on our first real classification example.

Consider the small town of Randomville, where people are crazy about their two sports teams, the Randomville Reds and the Randomville Blues. The Reds had been around for a long time, and people loved them. But then some out-of-town millionaire came along and bought the Reds' top scorer and started a new team, the Blues. To the discontent of most Reds fans, the top scorer would go on to win the championship title with the Blues. Years later he would return to the Reds, despite some backlash from fans who could never forgive him for his earlier career choices. But anyway, you can see why fans of the Reds don't necessarily get along with fans of the Blues. In fact, these two fan bases are so divided that they never even live next to each...

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