Meta-learning, often referred to as "learning to learn," is a subfield of machine learning that focuses on improving the learning process itself. It involves algorithms that can adapt to new tasks quickly by leveraging knowledge gained from previous experiences. This approach aims to enhance the efficiency and effectiveness of learning systems, making them more versatile in handling various challenges. In meta-learning, models are trained on a variety of tasks, allowing them to identify patterns and strategies that can be applied to new, unseen tasks. Techniques such as few-shot learning and transfer learning are commonly used, enabling systems to generalize better and reduce the amount of data needed for training on new tasks.