Skilvy

Teaching a machine to understand language

NLP (natural language processing) is the AI field teaching machines to understand and process human language: text and speech. It is what stands behind search, translators, voice assistants, chatbots and, of course, large language models (LLMs). Language is natural for a human but a most complex task for a machine: it is ambiguous, context-dependent, full of exceptions. This course gives a practical picture: which tasks NLP solves, how a machine represents text and how modern NLP arrived at LLMs.

Why language is hard for a machine

Ambiguity: "bank" — a river bank or a money bank? The meaning depends on context.
Context: one word changes meaning depending on the surroundings.
Variability: one thought can be expressed in many ways.
Exceptions: language is full of irregularities, idioms, slang.

What NLP solves

What tasks does NLP solve? Give real-life examples.

NLP covers everything related to a machine processing language. The main tasks (which the course covers): 1) Text classification: determine a texts category. Examples: sentiment (positive/negative of a review), email topic (spam/not spam, ticket category), user intent. 2) Information extraction: pull the needed data from text. Examples: find names, dates, amounts, organizations in text (entity recognition); extract key facts from a document. 3) Search and semantic similarity: find relevant texts by meaning (a link to the embeddings/vector-search course). Examples: smart search, similar recommendations, duplicate finding. 4) Translation: from one language to another (machine translation). 5) Summarization: compress a long text into a brief summary. 6) Question answering: find/generate an answer to a question over text (a link to RAG). 7) Text generation: create coherent text (what LLMs do powerfully). 8) Speech recognition and synthesis: text to speech (a link to the voice/multimodality course). Where it is applied in life: — Search engines: understand the query, find the relevant. — Voice assistants: understand a command, answer. — Translators: translate text/speech. — Email: spam filtering, auto-replies, prioritization. — Support: chatbots, ticket routing, sentiment analysis. — Analytics: analyzing reviews, social media, feedback (what customers think). — Documents: data extraction, classification, knowledge-base search. — And, of course, LLMs (ChatGPT-like) — the peak of modern NLP, uniting many tasks. The key idea: modern NLP, like computer vision, works on machine learning and neural networks (from the ML courses). Models LEARN language on huge amounts of text rather than being programmed with grammar rules. It is this (learning on data + powerful neural networks, especially the "transformer" architecture) that led to the LLM breakthrough. Previously people tried to build NLP on rules (nearly impossible for living language with its exceptions), now models learn to understand language from examples. The practical meaning for you: — NLP is the foundation on which LLMs and many AI text tools you already use stand. — Understanding NLP basics (how a machine represents text, what tasks exist, how it works), you better understand the capabilities and limitations of LLMs and text AI tools. — You do not need to be a researcher: there are ready models, services and LLMs for NLP tasks. The course gives a practical picture — what NLP can do, how text representation works, and how modern NLP (transformers, LLMs) changed the field. It will tie together much from previous courses (embeddings, LLMs, multimodality) through the lens of "how a machine works with language".

Learning on data, not grammar rules

The key idea: modern NLP, like computer vision, works on machine learning and neural networks. Models learn language on huge amounts of text rather than being programmed with grammar rules. Previously people tried to build NLP on rules (nearly impossible for living language with its exceptions, idioms, context), now models learn to understand language from examples. It is this — learning on data plus powerful neural networks (especially the "transformer" architecture, more later) — that led to the LLM breakthrough.

NLP — the foundation under LLMs

The practical meaning: NLP is the foundation on which LLMs and many text AI tools you already use stand (search, translators, assistants, chatbots). Understanding NLP basics (how a machine represents text, what tasks exist, how it works), you better understand the capabilities and limitations of LLMs. You do not need to be a researcher — there are ready models, services and LLMs. The course will tie together much from previous courses (embeddings, LLMs, multimodality) through the lens of "how a machine works with language".

Course rule: NLP teaches machines to understand and process language (classification, extraction, search, translation, generation etc.). It works through training neural networks on texts, not through grammar rules. It is the foundation under LLMs.

NLP is the AI field teaching machines to understand and process human language.

🧠 How does modern NLP teach machines to work with language?

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