Machine learning, explained
Google’s glossary defines it as a program or system that trains a model from input data, so the model can make useful predictions from new data. It works best when the new data looks like the data it learned from. If the past is a poor guide to the future, the predictions will be poor too.
Machine learning is a broad field. Large language models are one kind of machine learning model, built for text. Many business uses are smaller and plainer: a forecast of demand, a flag on a record that looks off, or an estimate with a range around it.
How does machine learning work?
You give it many past examples, and training adjusts a model until its guesses match those examples well. Then you use the trained model on new cases. Testing it on data it has not seen tells you if it really learned the pattern.
When is machine learning worth it for a business?
When you have enough history to learn from and a question you ask often, such as how much stock to order. If simple rules already do the job, keep the rules. We only switch to a learned model when it beats them in testing.
Questions
What is machine learning in simple terms?
Machine learning is software that learns patterns from past data, then uses what it learned to make predictions about new data.
Is machine learning the same as AI?
Machine learning is one part of AI. It covers systems that learn from data, including the large language models behind many chat tools.
How much data does machine learning need?
There is no single number. It needs enough past examples that look like the cases you will predict, and you test it on data it has not seen to check it learned.
Where we use this term
Sources
- Machine Learning Glossary. Google for Developers.
Definition checked against these sources and our own pages on .