his advanced computer vision course series focuses on building modern object detection, tracking, and segmentation systems using state-of-the-art AI models. Learners begin by understanding how to choose the right model architecture for different vision tasks, balancing speed, accuracy, and scalability. The series explores real-time tracking and counting pipelines using Roboflow workflows combined with ByteTrack-style tracking strategies. Students train custom object detectors using YOLOv8 and experiment with segmentation tasks using YOLOv7. Transformer-based detection is introduced through DETR, highlighting how attention-driven architectures improve recognition. The course also covers cutting-edge zero-shot detection with Grounding DINO and universal segmentation using Segment Anything Model, enabling flexible detection without retraining. Additional modules demonstrate training high-performance detectors like YOLO-NAS on custom datasets. Through practical tutorials, learners build full pipelines for detection, segmentation, and tracking using Python, preparing them to deploy scalable AI vision systems in automation, robotics, analytics, and smart monitoring applications.