Meta-learning is a subfield of machine learning that focuses on developing algorithms and models capable of learning how to learn. In other words, meta-learning aims to improve the efficiency and adaptability of machine learning systems by enabling them to continually learn and adapt from new tasks and experiences. This involves developing algorithms that can learn from a variety of different tasks and datasets, as well as explore different learning strategies and techniques to optimize their performance. Meta-learning is particularly useful in settings where a model needs to quickly adapt to new and unseen tasks, as well as in domains where data is limited or noisy.