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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

Working with Data in OpenCV and Python

Now that we have whetted our appetite for machine learning, it is time to delve a little deeper into the different parts that make up a typical machine learning system.

Far too often, you hear someone throw around the phrase, just apply machine learning to your data!, as if that will instantly solve all your problems. You can imagine that the reality of this is much more intricate. Although, I will admit that nowadays it is incredibly easy to build your own machine learning system simply by cutting and pasting just a few lines of code from the internet. However, in order to build a system that is truly powerful and effective, it is essential to have a firm grasp of the underlying concepts and an intimate knowledge of the strengths and weaknesses of each method. So don't worry if you aren't considering yourself a machine learning...

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