Foundations
ML fits problems where the rules are too messy to write by hand, like fraud detection or demand forecasting. It needs clean historical data to work, which is usually the real constraint.
In plain terms
Machine learning is a way of building software that learns patterns from examples instead of being programmed with explicit rules.
Also called ML.
Machine learning is the part of AI where the software improves from examples instead of from a rule someone typed. You show it past outcomes. It finds the pattern. Spam filters work this way. So do systems that guess which invoice line is the tax, or which support ticket should go to a specialist.
The pattern is only as good as the examples. If last year's examples are biased, thin, or labeled by people who disagreed, the system repeats that. You do not need to understand the math. You do need to ask who labeled the examples, how old they are, and what happens when a new kind of case shows up.
Think of it this way: Traditional software is a recipe with exact steps. Machine learning is a chef who learns what 'good' tastes like by eating thousands of dishes and then makes new ones from that understanding.
A SaaS company trains a churn model on two years of customer behavior. It now flags accounts at risk three weeks before they cancel, giving the sales team time to intervene.
A staffing firm wants to spot candidates who stay past a year. The system looks at past hires and flags a shortlist. If the old data only reflects who the team already hired, it will keep recommending the same kind of person. A hiring manager still makes the offer.
When you have historical examples of the right answer and the pattern is too complex to write as rules. Fraud detection, recommendations, and forecasting are textbook fits. When you lack clean historical data, when the environment changes faster than you can retrain, or when you need the model to explain its reasoning to regulators.
You do not need to pick a model from this page. RaftLabs starts from the job: the documents, the decision, and what a wrong answer costs. You see a working version before a large build. The related work on our side is Machine learning development.
This sits with the other foundations terms on the glossary. The words under every AI conversation, from the model itself to the data it learned from. Worth reading next: Artificial Intelligence (AI), Large Language Model (LLM), and Generative AI.
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