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)

First Steps in Supervised Learning

This is the moment you've been waiting for, isn't it?

We have covered all the bases-we have a functioning Python environment, we have OpenCV installed, and we know how to handle data in Python. Now it's time to build our first machine learning system! And what better way to start off than to focus on one of the most common and successful types of machine learning: supervised learning?

From the previous chapter, we already know that supervised learning is all about learning regularities in some training data by using the labels that come with it so that we can predict the labels of some new, never-seen-before test data. In this chapter, we want to dig a little deeper, and learn how to turn our theoretical knowledge into something practical.

Specifically, we want to address the following questions:

  • What's the difference between...