Hands-On AI: Computer Vision Projects with Ultralytics and OpenCV

Get an in-depth overview of computer vision algorithms in the YOLO family and demonstrate how to use these algorithms to address real-world challenges. This course includes technical walkthroughs for essential techniques like image classification, object detection, object tracking, instance segmentation, pose estimation, and oriented bounding boxes (OBB) using the Ultralytics Python package. Instructor Muhammad Munawar guides you through annotating data, training models, and exporting them, highlighting how the export process speeds up inference time. Additionally, see how Ultralytics solutions are tailored for solving practical computer vision challenges, with in-depth technical implementation examples provided throughout the course.

Learning objectives
Gain foundational and intermediate skills in computer vision, enabling you to apply for computer vision roles across various industries.
Tackle complex computer vision challenges from scratch and determine which techniques are best suited to specific problems.
Understand the fundamental workflow of a computer vision project.
Learn how to perform basic operations using OpenCV and how to use the well-known VisionAI package (Ultralytics).