Dimensionality Reduction
In plain terms
Reducing the number of features while preserving information.
Going deeper
Dimensionality reduction compresses high-dimensional data into fewer dimensions for visualization, speed, or noise reduction. It keeps the essence with less data.
Under the hood
PCA (linear) and t-SNE/UMAP (non-linear) are common; helps fight the curse of dimensionality.
Examples
PCA, t-SNE, UMAP, visualization, compression, preprocessing.
Think of it like...
Like summarizing a long report into its key points without losing the gist.