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Practical Machine Learning and Image Processing

For Facial Recognition, Object Detection and Pattern Recognition Using Python

Introduction
Practical Machine Learning and Image Processing gives readers deep
insight into the basics of image processing and various image processing
methodologies and algorithms, applications using various Python
libraries, and real-time use case implementation using machine learning
approaches.
The book begins with a discussion of the setup environment for
different operating systems, presents basic image processing terminology,
and explores useful Python concepts for algorithm application. It
then delves into various image processing algorithms and practical
implementation of them in Python using two libraries: Scikit Image and
OpenCV. Next, advanced machine learning and deep learning methods
are presented for image processing and classification. Concepts such as
Adaboost, XG Boost, convolutional neural networks, and more, for imagespecific applications are explained. Later, the process for making models in
real time and then deploying them is described.
All the concepts in the book are explained using real-life scenarios.
By the end of the book, readers should be able to apply image processing
techniques and make machine learning models for customized
applications.

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