Book description
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, handson guide.
About This Book
 Load, store, edit, and visualize data using OpenCV and Python
 Grasp the fundamental concepts of classification, regression, and clustering
 Understand, perform, and experiment with machine learning techniques using this easytofollow guide
 Evaluate, compare, and choose the right algorithm for any task
Who This Book Is For
This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical realworld tasks.
What You Will Learn
 Explore and make effective use of OpenCV's machine learning module
 Learn deep learning for computer vision with Python
 Master linear regression and regularization techniques
 Classify objects such as flower species, handwritten digits, and pedestrians
 Explore the effective use of support vector machines, boosted decision trees, and random forests
 Get acquainted with neural networks and Deep Learning to address realworld problems
 Discover hidden structures in your data using kmeans clustering
 Get to grips with data preprocessing and feature engineering
In Detail
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 selfdriving cars and Google's DeepMind.
OpenCV lies at the intersection of these topics, providing a comprehensive opensource library for classic as well as stateoftheart computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the opensource 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!
Style and approach
OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
Publisher resources
Table of contents
 Preface
 A Taste of Machine Learning

Working with Data in OpenCV and Python
 Understanding the machine learning workflow
 Dealing with data using OpenCV and Python
 Summary
 First Steps in Supervised Learning
 Representing Data and Engineering Features

Using Decision Trees to Make a Medical Diagnosis
 Understanding decision trees
 Using decision trees to diagnose breast cancer
 Using decision trees for regression
 Summary
 Detecting Pedestrians with Support Vector Machines
 Implementing a Spam Filter with Bayesian Learning

Discovering Hidden Structures with Unsupervised Learning
 Understanding unsupervised learning
 Understanding kmeans clustering
 Understanding expectationmaximization
 Compressing color spaces using kmeans
 Classifying handwritten digits using kmeans
 Organizing clusters as a hierarchical tree
 Summary
 Using Deep Learning to Classify Handwritten Digits
 Combining Different Algorithms into an Ensemble
 Selecting the Right Model with Hyperparameter Tuning
 Wrapping Up
Product information
 Title: Machine Learning for OpenCV
 Author(s):
 Release date: July 2017
 Publisher(s): Packt Publishing
 ISBN: 9781783980284
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