Datasets often have many more features than we could possibly process. For example, let's say our job was to predict a country's poverty rate. We would probably start by matching a country's name with its poverty rate, but that would not help us to predict the poverty rate of a new country. So, we start thinking about the possible causes of poverty. But how many possible causes of poverty are there? Factors might include a country's economy, lack of education, high divorce rate, overpopulation, and so on. If each one of these causes was a feature used to help to predict the poverty rate, we would end up with a countless number of features. If you're a mathematician, you might think of these features as axes in a high-dimensional space, and every country's poverty rate is then a single point in this high-dimensional...
Machine Learning for OpenCV 4
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Machine Learning for OpenCV 4
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Overview of this book
OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.
You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.
By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.
Table of Contents (18 chapters)
Preface
Free Chapter
Section 1: Fundamentals of Machine Learning and OpenCV
A Taste of Machine Learning
Working with Data in OpenCV
First Steps in Supervised Learning
Representing Data and Engineering Features
Section 2: Operations with OpenCV
Using Decision Trees to Make a Medical Diagnosis
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Section 3: Advanced Machine Learning with OpenCV
Using Deep Learning to Classify Handwritten Digits
Ensemble Methods for Classification
Selecting the Right Model with Hyperparameter Tuning
Using OpenVINO with OpenCV
Conclusion
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