This course teaches you computer vision through 30 hands-on projects, taking you from foundational image processing techniques to advanced concepts. You’ll learn to work with core computer vision libraries and frameworks including OpenCV, Scikit-learn, YOLO, and Detectron2, building practical skills in object detection, image classification, segmentation, and pose estimation. The course is structured to build your skills progressively, starting with fundamental image processing operations before introducing machine learning and deep learning methods for more complex visual recognition tasks.
Beyond model building, you’ll gain experience integrating computer vision into applications: deploying models to the cloud with AWS, building web applications with Streamlit and Langchain, creating APIs, and running inference on edge devices. You’ll also work with tools like AWS Rekognition and explore how vision models can be combined with LLMs to create more capable systems. You’ll also develop the ability to evaluate and compare different approaches, understanding trade-offs between models and how much data is needed for reliable performance.
By the end of this course, you’ll have the skills to design, train, and deploy computer vision systems, gaining experience across a wide range of practical use cases. Along the way, you’ll strengthen your understanding of key concepts such as feature extraction, model training, and dataset preparation, giving you a well-rounded foundation in computer vision.
