Machine learning is a specific approach to building AI systems in which the system learns from data rather than being given explicit instructions. Instead of a programmer writing rules for every situation, the system is trained on examples and develops its own internal rules.

There are three main types of machine learning. In supervised learning, the system is trained on labelled examples, such as thousands of emails marked as spam or not spam. In unsupervised learning, the system is given unlabelled data and has to find its own patterns and groupings. In reinforcement learning, the system learns by trial and error, receiving rewards for good outcomes and penalties for bad ones.

Machine learning is behind most of the AI applications people use daily. Your email spam filter, your streaming service recommendations, your bank’s fraud detection system, and the autocomplete on your phone are all machine learning applications.

Deep learning is a subset of machine learning that uses multi-layered neural networks. It is the engine behind large language models, image recognition systems, and most of the major AI breakthroughs of the past decade.

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