Transfer Learning is a machine learning technique where a model trained on one task or dataset is adapted or fine-tuned to perform another related task or dataset with minimal additional training. It leverages the knowledge and representations learned from the source domain to improve performance and efficiency on the target domain, especially when labeled data is limited or costly to obtain. Readers can explore how transfer learning approaches, such as feature extraction, fine-tuning, and domain adaptation, enable organizations to leverage pre-trained models and transfer knowledge across tasks and domains, accelerating model development and improving performance in various machine learning applications.