Practical Machine Learning and Image Processing

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 library: 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 conceptsin 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

Discover image-processing algorithms and their applications using Python

Explore image processing using the OpenCV library

Use TensorFlow, scikit-learn, NumPy, and other libraries

Work with machine learning and deep learning algorithms for image processing

Apply image-processing techniques to five real-time projects



Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.