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)

Understanding unsupervised learning

Unsupervised learning might come in many shapes and forms, but the goal is always to convert original data into a richer, more meaningful representation, whether that means making it easier for humans to understand or easier for machine learning algorithms to parse.

Some common applications of unsupervised learning include the following:

  • Dimensionality reduction: This takes a high-dimensional representation of data consisting of many features and tries to compress the data so that its main characteristics can be explained with a small number of highly informative features. For example, when applied to housing prices in the neighborhoods of Boston, dimensionality reduction might be able to tell us that the indicators we should pay most attention to are the property tax and the neighborhood's crime rate.
  • Factor analysis: This tries to find...