Machine Learning (ML)
Machine Learning (ML) is a branch of AI where systems learn patterns from data and improve at a task without being explicitly programmed for every rule.
How Machine Learning works
Instead of a programmer writing "if X then Y" rules, a machine learning model is trained on historical examples and learns the underlying pattern itself. Show it thousands of past loan applications and their outcomes, and it learns to predict risk on a new application.
Why it matters for your business
If you have historical data — sales, inventory, customer churn, support tickets — you likely have a machine learning opportunity sitting unused. ML is what powers demand forecasting, fraud detection, and churn prediction, all of which are approachable for small and mid-sized businesses today via low-code tools.
Frequently Asked Questions
How is machine learning different from a normal computer program?
A normal program follows rules a developer writes explicitly. An ML model is shown many examples (data) and learns the rules itself, so it can handle new, unseen cases.
What kind of data does ML need?
It depends on the task, but generally: enough relevant, reasonably clean historical examples of the outcome you want to predict — sales records, past support tickets, images, etc.