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Machine Learning for OpenCV: Intelligent image processing with Python
Authors: Michael Beyeler
Genres
Artificial Intelligence
Programming
Description:
Expand your OpenCV knowledge and understand key machine learning concepts using this practical guide
Key FeaturesLoad, store, edit, and visualize data using OpenCV and PythonGrasp the fundamental concepts of classification, regression, and clusteringUnderstand, perform, and experiment with machine learning techniquesBook DescriptionMachine learning is no longer just a buzzword, it is all around us - right from protecting your email and automatically tagging friends in pictures, through to predicting the movies you like. Computer vision is one of the most exciting fields for machine learning application, with deep learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV is significantly related to 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 get easy access to a wide range of open source computing libraries.
This book begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once you have learned the basics, you will explore a variety of algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionalities. As you progress, you'll build on your machine learning skills, and explore one of its most important concepts - deep learning. You'll even be able to use it for classifying handwritten digits. Finally, the book will summarize everything you've learned and help you tackle machine learning problems using the tools covered in the chapters.
By the end of this book, you will have the skills you need to address different machine learning challenges, either by building on the existing source code or developing your own algorithm from scratch.
What you will learnExplore and make effective use of OpenCV's machine learning moduleUnderstand deep learning for computer vision with PythonGet to grips with linear regression and regularization techniquesClassify objects such as flower species, handwritten digits, and pedestriansLearn the use of support vector machines, boosted decision trees, and random forestsGet familiar with neural networks and deep learning to address real-world problemsDiscover hidden structures in your data using k-means clusteringGet up to speed with data pre-processing and feature engineeringWho this book is forThis book is for Python programmers who are already familiar with OpenCV.
Table of ContentsA Taste of Machine LearningWorking with Data in OpenCV and PythonFirst Steps in Supervised LearningRepresenting Data and Engineering FeaturesUsing Decision Trees to Make a Medical DiagnosisDetecting Pedestrians with Support Vector MachinesImplementing a Spam Filter with Bayesian LearningDiscovering Hidden Structures with Unsupervised LearningUsing Deep Learning to Classify Handwritten DigitsCombining Different Algorithms into an EnsembleSelecting the Right Model with Hyperparameter TuningWrapping Up
Goodreads page:
https://www.goodreads.com/book/show/35712973-machine-learning-for-opencv
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