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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

Summary

In this book, we covered a lot of theory and practice.

We discussed a wide variety of fundamental machine learning algorithms, be it supervised or unsupervised, illustrated best practices as well as ways to avoid common pitfalls, and we touched upon a variety of commands and packages for data analysis, machine learning, and visualization.

If you made it this far, you have already made a big step toward machine learning mastery. From here on out, I am confident you will do just fine on your own.

All that's left to say is farewell! I hope you enjoyed the ride; I certainly did.

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