Machine learning is the engine powering most AI tools you use. Instead of being programmed with explicit rules, ML systems learn patterns from data and improve with experience.
How Machine Learning Works
Think of ML like teaching a child to recognize dogs. You don’t give them rules (“four legs, fur, tail”). You show them thousands of dog photos until they learn the pattern themselves. ML works the same way — feed it enough examples and it learns to make predictions about new, unseen data.
Key ML Concepts
Training Data
The examples ML learns from. Quality and quantity of training data directly determine model performance. ChatGPT was trained on billions of web pages; your spam filter learns from thousands of your emails.
Models
The mathematical representation that captures learned patterns. Different model architectures (neural networks, decision trees, transformers) suit different types of problems.
Inference
Using a trained model to make predictions on new data. When you ask ChatGPT a question, it’s performing inference — applying learned patterns to generate a response.
For the most impactful ML application, see Generative AI. For LLMs specifically, explore LLMs Explained.