This course provides a comprehensive overview of the principles, techniques, and applications of transfer learning in modern machine learning. Participants will explore core concepts, pretrained models, and advanced methods for adapting existing models to new tasks and datasets. The course also covers evaluation metrics, common challenges, and real-world applications, culminating in a hands-on project where learners build their own transfer learning pipeline.