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Meta-learning is a branch of machine learning focused on helping models learn new tasks more efficiently. Instead of training a completely new model from scratch every time, meta-learning teaches systems how to adapt quickly using only a small amount of data. Because of this, meta-learning…

Federated learning is a machine learning approach that allows multiple devices or organizations to train a shared AI model collaboratively without sending their raw data to a central server. Instead of moving sensitive information into one massive database, each participant trains the model locally on…

Semi-supervised learning is a machine learning approach that combines a small amount of labeled data with a much larger amount of unlabeled data. It sits between supervised learning and unsupervised learning. Instead of relying entirely on expensive labeled datasets, semi-supervised learning allows models to learn…

Reinforcement learning is a branch of machine learning where an AI agent learns by interacting with an environment and receiving feedback from its actions. Instead of learning from labeled examples, the agent improves through experience. It tries actions, receives rewards or penalties, and gradually learns…

Unsupervised learning is a branch of machine learning focused on discovering patterns, structures, and relationships inside data without using labeled answers. Unlike supervised learning, where the correct outputs are already known, unsupervised learning works with raw data and tries to uncover meaningful organization automatically. This…