Unlock the potential of image segmentation with this comprehensive course designed to merge theory with practical expertise. Begin with a strong foundation in PyTorch and image segmentation principles, setting up the environment and exploring the fundamental concepts of tensors, datasets, and neural networks. Early modules ensure a seamless transition for learners at all levels, providing clear guidance on data preprocessing and model setup. Progress into advanced topics where you'll dive deep into semantic segmentation, exploring state-of-the-art architectures such as UNet and Feature Pyramid Network. Learn about essential techniques like upsampling, loss functions, and evaluation metrics, enabling you to fine-tune models for accuracy. Through real-world projects, such as segmenting satellite images, the course emphasizes practical applications, bridging the gap between theory and industry needs. The hands-on approach culminates with model training and evaluation, focusing on metrics like pixel accuracy and Intersection over Union. You'll gain experience in setting up training loops, fine-tuning hyperparameters, and saving models for future use. By the end of the course, you will have mastered the tools and techniques necessary to tackle complex segmentation challenges confidently.