One-Shot Learning is a machine learning paradigm where models are trained to recognize new categories from only one or a few examples. This approach is particularly useful in scenarios where data collection is expensive or limited, such as in medical diagnosis, facial recognition, or rare object detection.One-Shot Learning offers exciting research opportunities, addressing the challenge of learning from limited data in a variety of contexts. From developing novel algorithms and architectures to exploring applications in domains such as healthcare, vision, and natural language processing, these projects have the potential to push the boundaries of how machines learn from minimal information.