Meta-learning, often referred to as "learning to learn," is a process where algorithms improve their learning capabilities by analyzing their own performance. This approach allows models to adapt quickly to new tasks by leveraging knowledge gained from previous experiences. In meta-learning, techniques such as transfer learning and few-shot learning are commonly used. These methods enable a model to generalize from limited data, making it more efficient in solving new problems. By focusing on the learning process itself, meta-learning aims to enhance the overall effectiveness of machine learning systems.