Classification, regression, clustering, and dimensionality reduction

Remember the differences among the four representative machine learning tasks.
・Classification: supervised learning that trains on labeled data and assigns an input to a category (discrete label) (for example, lesion / no lesion, dog / cat).
・Regression: also supervised learning, but it predicts a continuous number such as sales or temperature.
・Clustering: unsupervised learning that groups similar data without using labels (for example, customer segmentation).
・Dimensionality reduction: unsupervised learning that compresses features without using labels to make them easier to handle (used for visualization and preprocessing).
Identify them by output: category → classification, number → regression, unlabeled grouping → clustering, compression → dimensionality reduction.