Image Processing for Machine Learning is a comprehensive guide that bridges the gap between traditional image processing methods and the rapidly evolving field of machine learning. Designed for both researchers and practitioners, the book offers an in-depth exploration of how raw visual data can be transformed, enhanced, and interpreted to empower intelligent decision-making systems. As images become a dominant source of data in domains such as healthcare, autonomous vehicles, remote sensing, agriculture, robotics, and security, this book provides readers with the foundational knowledge and advanced techniques needed to harness visual data effectively. It explores the full pipeline—from image acquisition and preprocessing to feature extraction and model deployment—enabling readers to build robust, data-driven visual systems.

Key topics include image filtering, segmentation, color space transformations, feature engineering, convolutional neural networks (CNNs), data augmentation, and transfer learning. The book also covers state-of-the-art practices in real-time processing, edge AI integration, and performance evaluation, making it highly relevant for applications that require both speed and accuracy. One of the distinguishing features of this volume is its hands-on approach. Each concept is accompanied by real-world examples, Python code snippets, and illustrative case studies that demonstrate how machine learning techniques can be effectively applied to image-based tasks. Whether you are building a facial recognition model, developing smart medical diagnostics, or enhancing drone-based imaging, the book equips you with the tools and techniques necessary to succeed.

ISBN:`2987-3432-2228 | No. of Pages: 478 | Book Version: 104.0.6