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)

What this book covers

Chapter 1, A Taste of Machine Learning, will gently introduce you to the different subfields of machine learning, and explain how to install OpenCV and other essential tools in the Python Anaconda environment.

Chapter 2, Working with Data in OpenCV and Python, will show you what a typical machine learning workflow looks like, and where data comes in to play. I will explain the difference between training and test data, and show you how to load, store, manipulate, and visualize data with OpenCV and Python.

Chapter 3, First Steps in Supervised Learning, will introduce you to the topic of supervised learning by reviewing some core concepts, such as classification and regression. You will learn how to implement a simple machine learning algorithm in OpenCV, how to make predictions about the data, and how to evaluate your model.

Chapter 4, Representing Data and Engineering Features, will teach you how to get a feel for some common and well-known machine learning datasets and how to extract the interesting stuff from your raw data.

Chapter 5, Using Decision Trees to Make a Medical Diagnosis, will show you how to build decision trees in OpenCV, and use them in a variety of classification and regression problems.

Chapter 6, Detecting Pedestrians with Support Vector Machines, will explain how to build support vector machines in OpenCV, and how to apply them to detect pedestrians in images.

Chapter 7, Implementing a Spam Filter with Bayesian Learning, will introduce you to probability theory, and show you how you can use Bayesian inference to classify emails as spam or not.

Chapter 8, Discovering Hidden Structures with Unsupervised Learning, will talk about unsupervised learning algorithms such as k-means clustering and Expectation-Maximization, and show you how they can be used to extract hidden structures in simple, unlabeled datasets.

Chapter 9, Using Deep Learning to Classify Handwritten Digits, will introduce you to the exciting field of deep learning. Starting with the perceptron and multi-layer perceptrons, you will learn how to build deep neural networks in order to classify handwritten digits from the extensive MNIST database.

Chapter 10, Combining Different Algorithms into an Ensemble, will show you how to effectively combine multiple algorithms into an ensemble in order to overcome the weaknesses of individual learners, resulting in more accurate and reliable predictions.

Chapter 11, Selecting the Right Model with Hyper-Parameter Tuning, will introduce you to the concept of model selection, which allows you to compare different machine learning algorithms in order to select the right tool for the task at hand.

Chapter 12, Wrapping Up, will conclude the book by giving you some useful tips on how to approach future machine learning problems on your own, and where to find information on more advanced topics.