Machine Learning Basics

Supervised Machine Learning

Supervised machine learning is a type of machine learning where a model learns from labeled data—that is, data that already includes both the inputs (features) and the correct output (also called a label).

Unsupervised Machine Learning

Unsupervised machine learning is a type of machine learning where the model works with unlabeled data, meaning the data includes inputs (features) but no correct output or label.

Instead of learning to predict a known answer, the model looks for patterns, structures, or groupings within the data on its own.

Ensemble Learning

Ensemble learning is a machine-learning approach that builds and combines several individual models, often of different types, to tackle the same prediction task.

It blends the models’ answers—using techniques called bagging, boosting, or voting—so the final prediction is usually more accurate and dependable than what any single model could give.