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.
Last updated
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.
We build these ideas into real systems you own. Tell us what you're trying to do.