Object Detection with Deep Learning: Building Computer Vision Models

Object detection is at the heart of modern technology, powering everything from self-driving vehicles to medical diagnostic tools. Understanding how to locate and classify multiple objects within a single image is a critical skill for anyone entering the field of computer vision. This written course guides you from foundational deep learning concepts to training your own object detection models. You will learn the mechanics behind modern architectures and understand how to prepare datasets, evaluate model performance, and apply these models to real-world scenarios. What you'll learn: - Understand the fundamental terminology of computer vision, bounding boxes, and anchor boxes - Analyze modern object detection architectures including YOLO, SSD, and Faster R-CNN - Apply transfer learning techniques to train custom models using pre-trained backbones - Evaluate model performance using key metrics like Intersection over Union (IoU) and mean Average Precision (mAP) - Prepare and augment image datasets to improve model accuracy and robustness - Configure training pipelines and implement code-based solutions for object detection tasks The course begins with essential terminology and the mathematical foundations of spatial localization. You will then progress through structured written lessons and code walkthroughs to configure, train, and test a model to detect real-world objects like parking signs. This course is designed for beginners with a basic understanding of Python, and no prior experience in computer vision or deep learning is required to start. Start reading today and build the skills to make machines see.