What is machine learning?
Machine learning is a subset of artificial intelligence. Instead of writing rules by hand, you feed a model examples and let it learn the patterns that connect inputs to outputs. The more (good) data it sees, the better it gets.
A spam filter that improves as you mark messages spam or not-spam is doing machine learning. So is a recommendation engine, a fraud-detection model, or a system that predicts which patients are at risk of readmission.
How it differs from AI and deep learning
- Artificial intelligence is the broad goal of machines performing intelligent tasks.
- Machine learning is the method — learning from data.
- Deep learning is a specific technique within machine learning that uses many-layered neural networks, and powers most modern AI breakthroughs.
So all deep learning is machine learning, and all machine learning is AI — but not the reverse.
The main types of machine learning
- Supervised learning — the model learns from labeled examples (this email is spam, that one isn't).
- Unsupervised learning — the model finds structure in unlabeled data (grouping similar customers together).
- Reinforcement learning — the model learns by trial and error, receiving rewards for good outcomes.
Where it's used
Machine learning sits behind product recommendations, search ranking, ad targeting, speech recognition, forecasting, and the large language models driving modern chatbots. If a product gets better the more you use it, machine learning is usually why.