Glossary

Machine learning is the branch of artificial intelligence in which a system gets better at a task by finding patterns in data, instead of following rules a programmer wrote out by hand.

A neural network is a model made of layers of simple connected units whose connection strengths are tuned, through training, until the whole network maps inputs to the outputs you want.

Deep learning is machine learning with many-layered neural networks that learn their own features directly from raw data, rather than relying on features a human picks out first.

Generative AI is a class of models that produce new content — text, images, audio or code — by learning the patterns of a huge body of examples and then sampling fresh combinations from them.

A large language model is a neural network trained on vast amounts of text to predict the next piece of text, which turns out to be enough to make it write, summarise, translate and reason in language.

The Transformer is a neural-network architecture that processes a whole sequence at once and uses an 'attention' mechanism to weigh how much each part should influence every other part.

Prompt engineering is the practice of writing the input to a generative model carefully — instructions, context and examples — so that it reliably produces the output you actually want.

Retrieval-augmented generation is a pattern that fetches relevant documents at question time and puts them into a model's prompt, so its answer is grounded in specific, current sources rather than memory alone.

Natural language processing is the field of AI concerned with getting computers to work with human language — understanding it, generating it, translating it and pulling meaning from it.

Computer vision is the field of AI that gets machines to interpret images and video — detecting objects, classifying scenes, reading text in pictures and tracking what moves.

Agentic AI is the step from models that answer to systems that act: software built around a language model that plans a task, calls tools and APIs, checks its own progress and keeps going across multiple steps with limited human intervention.

MLOps is the engineering discipline of getting machine-learning models out of notebooks and into production — versioning, deploying, monitoring and retraining them — so they keep working after the world stops looking like the training data.

Responsible AI is the practice of building and deploying AI systems whose outcomes you can defend — fair, explainable, private and accountable — treating 'should we, and under which safeguards?' as an engineering requirement rather than an afterthought.

Data analysis is the process of examining data to answer a question — cleaning it, exploring it, and drawing conclusions you can act on and defend.

Data wrangling is the work of turning raw, messy data into a clean, consistent shape that analysis or a model can actually use.

Data visualization is the practice of representing data graphically so that patterns, comparisons and outliers become obvious to a human eye.

Data engineering is the discipline of building the pipelines and storage that move data reliably from where it is created to where it can be analysed or fed to a model.

Python is a general-purpose programming language prized for readable syntax and a vast ecosystem of libraries, which has made it the default language of data and AI.

Digital marketing is the practice of reaching and converting customers through digital channels — search, social, email and the web — measured and optimised with data.

SEO is the craft of being the answer: shaping a site's content, structure and technical health so that when someone searches for what you offer, search systems find you, understand you and rank you.

Marketing automation is software doing the follow-up a human team would forget: triggered, personalised sequences of emails, messages and audience updates that respond to what each contact actually does, at a scale no team could handle by hand.