Applied Object Detection & Segmentation

This specialization prepares you to build, evaluate, and deploy production-ready object detection and image segmentation systems. Across eight hands-on courses, you'll learn to create quality-controlled vision datasets, train and evaluate models using metrics like mAP, IoU, and Dice, and diagnose performance issues through slice-level analysis and error logging. You'll build real-time detection pipelines with YOLOv8 and DeepSORT, refine segmentation outputs using post-processing techniques like CRF smoothing, and optimize models for edge deployment with TensorFlow Lite. The program also covers deploying scalable inference workflows using Docker and AWS Lambda, calibrating confidence scores for trustworthy predictions, and communicating results to technical and non-technical stakeholders. By completion, you'll have the end-to-end skills to take computer vision models from notebooks to reliable, production-grade systems

• Build and evaluate object detection and segmentation pipelines using metrics like mAP, IoU, Dice, and class-wise analysis
• Deploy vision models to production using Docker APIs, AWS Lambda serverless workflows, and TensorFlow Lite edge optimization
• Diagnose and improve model performance through error analysis, calibration techniques, and post-processing refinement methods

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work