Computer Vision and Machine Learning with MATLAB

Digital images contain vast amounts of data, but turning those pixels into actionable insights requires specialized machine learning techniques. This course guides you through the full machine learning lifecycle, enabling you to transform raw image data into intelligent systems that can recognize patterns and locate objects automatically. You will progress from understanding basic image data structures to implementing advanced detection algorithms. By the end of the course, you will be able to manage data preparation, train models for specific recognition tasks, and assess their performance using industry-standard metrics. What you'll learn: - Understand the fundamental concepts of digital image processing and machine learning. - Prepare and label image datasets for training robust computer vision models. - Build image classification systems using modern convolutional neural network architectures. - Implement object detection workflows to identify and locate specific items within an image. - Apply transfer learning techniques to leverage pre-trained models for faster development. - Evaluate model performance using standard metrics like accuracy and precision-recall. The course begins with foundational terminology and data handling before moving into practical workflows for training and refining models. You will explore how these techniques apply to scenarios such as identifying street signs and detecting material defects through written explanations and code-based exercises. This course is designed for beginners in engineering, science, or data analysis who want to start using computer vision tools. No prior experience with machine learning is required. Start building your own computer vision applications through clear, text-based instruction.