Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Where to go from here?

The goal of this book was to introduce you to the world of machine learning and prepare you to become a machine learning practitioner. Now that you know everything about the fundamental algorithms, you might want to investigate some topics in more depth.

Although it is not necessary to understand all the details of all the algorithms we implemented in this book, knowing some of the theory behind them might just make you a better data scientist.

If you are looking for a more advanced lecture, then you might want to consider some of the following classics:
  • Stephen Marsland, Machine Learning: An Algorithmic Perspective. Second Edition, Chapman and Hall/Crc, ISBN 978-146658328-3, 2014
  • Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer, ISBN 978-038731073-2, 2007
  • Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of...