Meta-Learning is a subfield of machine learning focused on improving the learning process itself. Instead of just learning to perform specific tasks, meta-learning algorithms analyze previous tasks and their outcomes to understand how to learn more efficiently. This approach allows models to adapt quickly to new tasks with limited training data by leveraging knowledge gained from prior experiences. Techniques like few-shot learning, where models are trained on only a few examples, are often rooted in meta-learning principles. Applications of meta-learning include robotics, where systems must adapt to changing environments, and natural language processing, where models need to handle diverse language tasks.