Machine learning (ML) is a way of developing computer systems that learn patterns from data to improve performance on a task. NIST defines it as “the development and use of computer systems that adapt and learn from data with the goal of improving accuracy.” In practice, an ML model uses patterns it has learned to predict a value, classify an item, find groupings, choose an action, or generate content.
What machine learning means
Machine learning is part of artificial intelligence (AI). Instead of relying only on rules explicitly written for every case, an ML system derives a mathematical relationship from examples or other data and applies it to a task. A model might, for example, estimate a house price from its features or decide which category best fits an item.
Learning does not mean a computer becomes conscious or automatically improves forever. A model is built through a learning process, and whether it is updated after deployment is a separate design choice.
How machine learning works
At a high level, a learning process uses data to create a model, then the model applies what it learned to new inputs. The development work can include preparing the data, selecting or engineering useful features, tuning an algorithm, training, and testing. NIST describes these as stages in its September 2024 overview of machine learning.
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- Prepare examples or data. The information must be suitable for the task; in supervised learning, examples include known answers.
- Train a model. A learning process derives patterns or relationships from the training data.
- Evaluate the model. Check its predictions against actual outcomes using data it did not train on. Strong results on training examples alone do not show that it will work well on new cases.
- Use the model for the task. Depending on the system, it may predict, classify, group, select actions, or produce content.
Performance depends in part on the size, quality, and diversity of the data. Testing on unseen examples helps assess whether a model generalizes beyond the cases it has already encountered.
The main machine-learning approaches
The approaches differ mainly in the learning signal: known answers, patterns without supplied answers, or feedback from actions.
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| Approach | Learning signal | Typical goal | Example |
|---|---|---|---|
| Supervised learning | Examples paired with known labels or values | Predict a value or assign a category | Estimate a house price or classify an item |
| Unsupervised learning | Unlabeled data | Find patterns or group similar examples | Cluster weather patterns |
| Reinforcement learning | Feedback, often represented as rewards, after actions in an environment | Improve a sequence of choices | Choose actions in a game or guide a robot |
Supervised learning
In supervised learning, the model sees examples with known answers and learns a relationship it can use on new data. NIST defines it as a type of ML in which a model learns to predict explicit, often human-generated, labels or output values. Predicting a numeric value is commonly called regression; choosing among categories is classification.
Unsupervised learning
Unsupervised learning looks for structure in data that has no supplied answer labels. Clustering can group similar data points, but a group is not automatically a meaningful human category. People may need to interpret what the groups represent in the context of the task.
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Reinforcement learning
In reinforcement learning, an agent takes actions in an environment and receives feedback represented by rewards. It learns to improve its behavior according to that reward signal. Robotics and game playing are examples of tasks where this approach can be used.
Where generative AI fits
Generative AI describes systems that produce content, such as text, images, or music. It is best understood as a kind of task or output, not as a fourth learning approach equivalent to supervised, unsupervised, and reinforcement learning. These descriptions can overlap: a generative model uses machine-learning techniques, while the way it is trained is a separate question.
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Machine learning, AI, and deep learning
- Artificial intelligence is the broader field. NIST includes a definition of AI as a set of techniques, including machine learning, designed to approximate a cognitive task. Not every AI system is necessarily machine learning.
- Machine learning is a family of methods within AI that uses data to learn patterns or relationships for a task.
- Deep learning is a subset of machine learning that uses neural networks.
- Generative AI refers to systems that create content; it can use ML and is not a separate rung in the AI–ML–deep-learning hierarchy.
What to remember when evaluating an ML result
- A model’s performance on its training data is not enough to show how it will perform on unfamiliar examples; evaluation should include unseen data.
- Data quality and diversity affect the patterns a model can learn and how well it may generalize.
- An unsupervised cluster does not explain itself: finding groups and deciding what they mean are different steps.
- Training a model and updating it after deployment are distinct choices; an ML system does not necessarily learn continuously in use.
For introductory lessons and examples of regression, classification, clustering, reinforcement learning, and generative AI, see Google for Developers’ introduction to machine learning. NIST’s glossary provides definitions of machine learning, supervised learning, unsupervised learning, reinforcement learning, and artificial intelligence.
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