Machine Learning for OpenCV

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

Machine Learning for OpenCV

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Michael Beyeler

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.
This title is available to pre-order now and is expected to be published in
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Book Details

ISBN 139781783980284
Paperback380 pages

Book Description

OpenCV is an open source computer vision and machine learning library. Designed for computational efficiency, OpenCV has a strong focus on real-time applications. This book gives you an opportunity to make the most of machine learning for OpenCV

This book begins by introducing you to the essential subfields of machine learning and installation of OpenCV and working with training data in OpenCV. You will then learn supervised learning, by reviewing some of the core machine learning concepts of classification and regression,with the help of simple and intuitive examples. You also dive into data preprocessing and feature engineering. Using open-source tools and libraries, you will then explore various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. Following this you will learn about unsupervised learning and how to use k-means clustering and Expectation Mazimization in OpenCV to discover hidden structures in simple, unlabeled datasets. Now you will move on to deep learning and get the insights of perceptrons, artificial neural networks & Keras . You will also learn ensemble learning, where multiple models and classification algorithms are combined to form a new , more powerful, ensemble classifier. As the book ends, you will get to tweak the hyperparameters of an machine learning algorithm using cross-validation and grid search in OpenCV.

By the end of this book, you will be able to make the most of Machine Learning with the help OpenCV.

Table of Contents

What You Will Learn

  • Explore and make effective use of OpenCV's machine learning module
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and traffic signs
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and deep learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
  • Evaluate, compare, and choose the right algorithm for the task

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Table of Contents

Book Details

ISBN 139781783980284
Paperback380 pages
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