What is NLP?
Teaching computers to read
Natural Language Processing (NLP) is the field that gives computers the ability to read, understand, and generate human language — text and speech.
Numbers and spreadsheets are easy for machines. Language is the opposite: it's ambiguous, full of context, sarcasm, idioms, and exceptions. NLP is the bridge between the messy way humans communicate and the precise numbers a model needs.
NLP turns unstructured language into structure a computer can act on — and turns a computer's output back into fluent language.
Why language is hard for machines
The animation shows a sentence a human reads instantly, then the ambiguity a computer must untangle, then the many tasks NLP breaks language down into.
What makes it tricky
The same word can be a noun or a verb. Meaning depends on context.
Tone flips meaning — "sick" can be bad or great depending on the situation.
A thousand ways to say the same thing; spelling, slang, and typos abound.
Resolving "it" needs knowing trophies are big and suitcases hold things.
The everyday applications
Understanding what you actually mean by a query or command.
Real-time translation between hundreds of languages.
Gauging opinion and flagging toxic or spam content at scale.
Generating fluent, helpful responses — the frontier of modern NLP.
How this track is organized
NLP turns into machine learning once text becomes numbers. This track walks that path:
Clean the text (tokenization, stop words, stemming) → turn it into numbers (Bag of Words, TF-IDF) → build models (spam & sentiment classifiers) → capture meaning (word embeddings) → modern foundations (transformers, BERT).
Start with the NLP Pipeline for the bird's-eye view of those stages.