Machine learning: Difference between revisions

==Compute==

Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms and models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. The aim is to create systems and models which can automatically improve their performance over time based on experience, thus solving real-world problems.

Supervised Learning: In supervised learning, an algorithm is trained on a labeled dataset where each input and its associated label are known. The goal is to learn a mapping between input and output so that the model can accurately predict future data without being informed beforehand.

2. Unsupervised Learning: Unsupervised learning involves training an algorithm on an unlabeled dataset, where the output or label for each input is unknown. The goal is to uncover patterns or structure within the data by grouping similar examples together.

3. Reinforcement Learning: Reinforcement learning is an approach in which an algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. It receives feedback in the form of rewards or penalties, which it uses to adjust its decision-making strategy accordingly.