This short course gives you practical experience training and evaluating computer vision models. You’ll learn how to build image preprocessing pipelines, apply data augmentation, and train deep learning models such as CNNs and Vision Transformers. You’ll also learn to evaluate performance using metrics such as mean Average Precision (mAP), Intersection over Union (IoU), precision, and recall, and to use error analysis to understand failure patterns. Through short videos, focused readings, hands-on labs, and guided coaching, you’ll practice real job tasks such as writing TensorFlow data loaders, training a Vision Transformer on plant-disease images, computing per-class AP and mAP, and comparing results across IoU thresholds. By the end, you’ll have a complete workflow you can adapt to your own projects and use to demonstrate your skills.