Machines don't read text the way humans do — they need language converted into numbers before any model can work with it. NLP is the field dedicated to making that conversion useful.
Tokenization and Cleaning
The first step breaks text into smaller units — words or subwords — called tokens. Along the way, text is typically cleaned: lowercased, stripped of unnecessary punctuation, and sometimes reduced to root word forms so that "running" and "run" are treated as related.
From Words to Vectors
Word embeddings map each token to a list of numbers (a vector) positioned so that semantically similar words end up near each other in that numerical space. This is what allows a model to understand that "king" and "queen" are related concepts, despite never being told so explicitly.
Our Advanced AI and Machine Learning + NLP module walks through this pipeline end-to-end, from raw text to a working text classification model.
