Machine learning is a branch of artificial intelligence in which computer systems use data to identify patterns and support predictions or decisions. It is used in many settings, from recommendation systems to forecasting and image recognition.
Key ideas to understand
- What data represents in a machine-learning project
- How training and testing data differ
- Why model evaluation matters
- How bias and data quality affect results
Why the fundamentals matter
A typical machine-learning workflow begins with a clearly defined problem and relevant data. Data is prepared, a model is trained, and its performance is evaluated on data it has not seen during training. Evaluation helps reveal whether a model is useful and where it may fail. Results depend on data quality, the choice of method and the context in which the model is used.
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