Computer Vision
Image classification, object detection, segmentation and multimodal vision models.
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Computer vision courses teach machines to interpret images and video. Topics run from image preprocessing and augmentation with OpenCV through convolutional architectures, transfer learning from pretrained backbones, object detection with YOLO and similar families, semantic and instance segmentation, and the vision transformers and multimodal models now competing with them. Applied modules cover OCR, face and pose analysis, video tracking and deployment to edge devices where compute is limited. Common use cases include manufacturing quality inspection, retail analytics, medical imaging and autonomous systems. Deep learning fundamentals are assumed by most courses in this section.