What is machine learning?
Machine learning is a branch of artificial intelligence in which software learns patterns from data instead of following only hand-written rules. It uses those patterns to make predictions, classify items, or make decisions on new data.
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How machine learning works
A model is trained on example data, adjusting itself until it can find the patterns that link inputs to outcomes. Once trained, it applies what it learned to data it hasn't seen before.
The quality and relevance of the training data strongly affect how well it performs.
Machine learning in marketing
Marketers meet machine learning in automated bidding, lead scoring, audience modeling, recommendation systems, and forecasting. Many ad platforms use it behind the scenes to decide who sees an ad and what to bid.
These systems improve with good, consistent data, and they can slip in accuracy if conditions change.
How it relates to other AI terms
Machine learning is a subset of AI, and generative AI is a subset of machine learning that creates new content. Large language models are one kind of machine learning model.
Common questions
What's the difference between AI and machine learning?
AI is the broad goal of making software act intelligently. Machine learning is one common way to achieve it, by learning from data.
What are the main types of machine learning?
The three main types are supervised learning, which learns from labeled examples, unsupervised learning, which finds structure in unlabeled data, and reinforcement learning, which learns from rewards and penalties.
Does machine learning need a lot of data?
Usually it needs enough relevant, good-quality data to find reliable patterns. The amount depends on the task and the model.
Where does machine learning appear in ad platforms?
It's used in features such as automated bidding, audience expansion, and creative optimization. The platforms don't always explain exactly how each decision is made.
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