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)

Preprocessing data

The more disciplined we are in handling our data, the better results we are likely to achieve in the end. The first step in this procedure is known as data preprocessing, and it comes in (at least) three different flavors:

  • Data formatting: The data may not be in a format that is suitable for us to work with. For example, the data might be provided in a proprietary file format, which our favorite machine learning algorithm does not understand.
  • Data cleaning: The data may contain invalid or missing entries, which need to be cleaned up or removed.
  • Data sampling: The data may be far too large for our specific purpose, forcing us to sample the data in a smart way.

Once the data has been preprocessed, we are ready for the actual feature engineering: to transform the preprocessed data to fit our specific machine learning algorithm. This step usually involves one...