Natural Language Processing (NLP) is the branch of artificial intelligence concerned with enabling computers to understand, interpret, and generate human language in ways that are both meaningful and useful. At its core, NLP bridges the gap between human communication and machine processing by converting unstructured text into structured representations that algorithms can operate on. The field spans everything from simple text preprocessing tasks like tokenization and stemming to advanced contextual understanding through transformer-based models. A key mental model is the processing pipeline: raw text enters through preprocessing stages (cleaning, tokenization, normalization), transforms into numerical representations (embeddings, vectors), and flows through analysis layers (syntactic, semantic) to produce actionable insights or generated language. Understanding this progression—from words as symbols to words as vectors in semantic space—unlocks the ability to build systems that not only parse language but comprehend context, intent, and meaning.

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