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 image specific 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.