Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python

by Himanshu Singh

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing.
The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools.
All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.
What You Will Learn
Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.

184 pages, Paperback

Published February 27, 2019

Reviews

[Oskar Kosciolek · Rating 2 out of 5]

When rading it is easy to notice that the author has never written any another programming book. This book is not for newbies or for advanced programmers. The author often refers to a "glossary of important terms" and gives the reader a lot of code without almost any translation of what is in it. Only after 100 pages does he write that the book is for more advanced users, even though by that time the new user would probably not understand anything.
Nevertheless, the book actually contains quite a lot of examples from life and if someone has any idea about image processing and machine learning, they can find in this book quite useful examples of code, which after a small patch can be used for something usefull.

[Johnny · Rating 1 out of 5]

Another disappointing collection of notes. Spends too many pages on Python basics and then lacks the pages for the machine learning topics that this book should be about. (125 pages intro, 35 on machine learning)