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Machine Learning for OpenCV 4

You're reading from   Machine Learning for OpenCV 4 Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789536300
Length 420 pages
Edition 2nd Edition
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Authors (4):
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Aditya Sharma Aditya Sharma
Author Profile Icon Aditya Sharma
Aditya Sharma
Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning and OpenCV FREE CHAPTER
2. A Taste of Machine Learning 3. Working with Data in OpenCV 4. First Steps in Supervised Learning 5. Representing Data and Engineering Features 6. Section 2: Operations with OpenCV
7. Using Decision Trees to Make a Medical Diagnosis 8. Detecting Pedestrians with Support Vector Machines 9. Implementing a Spam Filter with Bayesian Learning 10. Discovering Hidden Structures with Unsupervised Learning 11. Section 3: Advanced Machine Learning with OpenCV
12. Using Deep Learning to Classify Handwritten Digits 13. Ensemble Methods for Classification 14. Selecting the Right Model with Hyperparameter Tuning 15. Using OpenVINO with OpenCV 16. Conclusion 17. Other Books You May Enjoy

Getting started with machine learning

Machine learning has been around for at least 60 years. Growing out of the quest for artificial intelligence, early machine learning systems inferred the hand-coded rules of if...else statements to process data and make decisions. Think of a spam filter whose job is to parse incoming emails and move unwanted messages to a spam folder as shown here in the following diagram:

We could come up with a blacklist of words that, whenever they show up in a message, would mark an email as spam. This is a simple example of a hand-coded expert system. (We will build a smarter one in Chapter 7, Implementing a Spam Filter with Bayesian Learning.)

These expert decision rules can become arbitrarily complicated if we are allowed to combine and nest them in what is known as a decision tree (Chapter 5, Using Decision Trees to Make a Medical Diagnosis). Then...

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