Feature extraction is the process of transforming raw data into a set of informative, non-redundant numerical features that machine learning models can use. In computer vision, a CNN automatically extracts edges and textures; in NLP, tokenizers convert words to IDs. Handcrafted features were common before deep learning; now models learn features automatically, though feature engineering remains valuable for tabular business data like customer demographics and transaction histories.