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)

Using Decision Trees to Make a Medical Diagnosis

Now that we know how to handle data in all shapes and forms, be it numerical, categorical, text, or image data, it is time to put our newly gained knowledge to good use.

In this chapter, we will learn how to build a machine learning system that can make a medical diagnosis. We aren't all doctors, but we've probably all been to one at some point in our lives. Typically, a doctor would gain as much information as possible about a patient's history and symptoms in order to make an informed diagnosis. We will mimic a doctor's decision-making process with the help of what is known as decision trees.

A decision tree is a simple yet powerful supervised learning algorithm that resembles a flow chart; we will talk more about this in just a minute. Other than in medicine, decision trees are commonly used in fields such...