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)

Summary

If you ask me, this chapter went by really fast. We learned all about decision trees, and how to apply them to both classification and regression tasks. We talked a bit about overfitting, and ways to avoid this phenomenon by tweaking pre-pruning and post-pruning settings. We also learned about how to rate the quality of a node split using metrics such as the Gini impurity and information gain. Finally, we applied decision trees to medical data in order to detect cancerous tissues. We will come back to decision trees towards the end of the book, when we will combine multiple trees into what is known as a random forest. But for now, let's move on to a new topic.

In the next chapter, we will introduce another staple of the machine learning world: support vector machines.