Expanded Explanation
Machine Learning (ML) is the branch of Artificial Intelligence where models learn from data — training on examples to make predictions or decisions. It powers recommendations, fraud detection, forecasting and the underlying training of large language models.
Simple explanation
Instead of telling a computer every rule, you show it examples and let it figure out the pattern by itself.
Technical explanation
Machine Learning includes supervised, unsupervised and reinforcement learning. Models are trained by minimising a loss function over a dataset, typically using gradient descent, and evaluated on held-out data.
Simple Examples
- Netflix recommending shows based on your watch history.
- A bank flagging an unusual credit card transaction.
- Predicting house prices from square footage and location.
Common mistakes
- Confusing correlation with causation in model outputs.
- Training on biased data and expecting fair predictions.
- Skipping evaluation and shipping a model that fails on new data.