Few Shot Learning refers to models that can generalize to new tasks from only a few examples. Unlike traditional models requiring large volumes of data, Few Shot Learning leverages small datasets for efficient learning, drawing parallels to human-like adaptability. It's crucial for tasks where data is scarce, such as medical diagnostics. Related is 'Zero Shot Learning,' which requires no examples, highlighting the model's grasp of broader patterns.