Glossary
Machine Learning (ML)
Definition
Machine learning (ML) is software that learns patterns from examples rather than being explicitly programmed with rules — so it can make predictions or decisions on data it hasn't seen before.
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Key points
- Instead of coding every rule by hand, you show the system examples and it learns the pattern — ideal when the rules are too complex to write out.
- Powers everyday business use cases: forecasting demand, scoring risk, detecting fraud, recommending products.
- A model is only as good as its data; quality, relevance, and freshness of examples drive the results.
- It's the broad field; deep learning and large language models are specific, more advanced branches of it.
Quick answer
Machine Learning — common question
AI is the broad goal of making software behave intelligently. Machine learning is the most common way to get there today — learning patterns from data rather than following hand-written rules. Most systems people call "AI" are built on machine learning.
It depends on the problem. Some tasks work with a few hundred good examples; others need far more. Just as important as volume is quality and relevance — clean, representative data beats a large but messy dataset.
From concept to working product.
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