Artificial Intelligence Terms: A to Z Glossary

Written by Coursera • Updated on

Review common artificial intelligence (AI) terms to know when preparing for an exam, interviews, or job search.

AI Glossary Terms

As you embark on a career in artificial intelligence (AI), it's a good idea to become familiar with common industry terms and concepts. This list of AI words provides an overview of key topics in AI and machine learning. You can use this AI terminology to build your vocabulary when studying for a Professional Certificate, drafting a resume, or interviewing for an AI job. Doing so can make it easier to present your qualifications to employers and have meaningful conversations about AI with others in this field.

Whether you are starting your career or switching from another field, feel confident and ready for the journey ahead with this list of AI terms.

AI terms

AI (artificial intelligence)

AI stands for artificial intelligence, which is the simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making.

Learn more: Expand your understanding of intelligent systems by exploring AI Courses.

AI ethics

AI ethics refers to the issues that AI stakeholders such as engineers and government officials must consider to ensure that the technology is developed and used responsibly. This means adopting and implementing systems that support a safe, secure, unbiased, and environmentally friendly approach to artificial intelligence.

Learn more: Navigate the complexities of moral guidelines in technology by exploring AI Ethics Courses.


An algorithm is a sequence of rules given to an AI machine to perform a task or solve a problem. Common algorithms include classification, regression, and clustering.

Application programming interface (API)

An API, or application programming interface, is a set of protocols that determine how two software applications will interact with each other. APIs tend to be written in programming languages such as C++ or JavaScript.

Learn more: Discover how to integrate AI functionalities into applications with API Courses.

Big data

Big data refers to the large data sets that can be studied to reveal patterns and trends to support business decisions. It’s called “big” data because organizations can now gather massive amounts of complex data using data collection tools and systems. Big data can be collected very quickly and stored in a variety of formats.

Learn more: Unlock the power of large datasets by exploring Big Data Courses essential for AI-driven analytics.


A chatbot is a software application that is designed to imitate human conversation through text or voice commands.

Learn more: Learn to design and deploy interactive AI assistants with Chatbot Development Courses.

Cognitive computing

Cognitive computing is essentially the same as AI. It’s a computerized model that focuses on mimicking human thought processes such as pattern recognition and learning. Marketing teams sometimes use this term to eliminate the sci-fi mystique of AI.

Computer vision

Computer vision is an interdisciplinary field of science and technology that focuses on how computers can gain understanding from images and videos. For AI engineers, computer vision allows them to automate activities that the human visual system typically performs.

Learn more: Explore the techniques to enable machines to interpret visual data with Computer Vision Courses.

Data mining

Data mining is the process of sorting through large data sets to identify patterns that can improve models or solve problems.

Learn more: Uncover patterns and insights from large datasets with Data Mining Courses, a critical skill in AI applications.

Data science

Data science is an interdisciplinary field of technology that uses algorithms and processes to gather and analyze large amounts of data to uncover patterns and insights that inform business decisions.

Learn more: Explore the intersection of statistics, machine learning, and data analysis with Data Science Courses.

Deep learning

Deep learning is a function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision.

Learn more: Master complex neural networks and advanced AI models with Deep Learning Courses.

Emergent behavior

Emergent behavior, also called emergence, is when an AI system shows unpredictable or unintended capabilities.

Generative AI

Generative AI is a type of technology that uses AI to create content, including text, video, code and images. A generative AI system is trained using large amounts of data, so that it can find patterns for generating new content.

Learn more: Learn how to create advanced synthetic content and models with Generative AI Courses, focusing on techniques like GPT and DALL-E.


Guardrails refers to restrictions and rules placed on AI systems to make sure that they handle data appropriately and don't generate unethical content.


Hallucination refers to an incorrect response from an AI system, or false information in an output that is presented as factual information.


A hyperparameter is a parameter, or value, that affects the way an AI model learns. It is usually set manually outside of the model.

Image recognition

Image recognition is the process of identifying an object, person, place, or text in an image or video. 

Large language model

A large language model (LLM) is an AI model that has been trained on large amounts of text so that it can understand language and generate human-like text.

Learn more: Explore the power of language understanding and generation with Large Language Models Courses, focusing on technologies like GPT and BERT. 

Limited memory

Limited memory is a type of AI system that receives knowledge from real-time events and stores it in the database to make better predictions.

Machine learning

Machine learning is a subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.

Learn more: Unlock the potential of predictive analytics and algorithmic processing with Machine Learning Courses.

Natural language processing

Natural language processing (NLP) is a type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices.

Learn more: Enhance your understanding of how machines process human language with Natural Language Processing (NLP) Courses.

Neural network

A neural network is a deep learning technique designed to resemble the human brain’s structure. Neural networks require large data sets to perform calculations and create outputs, which enables features like speech and vision recognition.

Learn more: Dive into the fundamentals and advanced concepts of artificial neural networks with Neural Networks Courses.


Overfitting occurs in machine learning training when the algorithm can only work on specific examples within the training data. A typical functioning AI model should be able to generalize patterns in the data to tackle new tasks.

Pattern recognition

Pattern recognition is the method of using computer algorithms to analyze, detect, and label regularities in data. This informs how the data gets classified into different categories.

Predictive analytics

Predictive analytics is a type of analytics that uses technology to predict what will happen in a specific time frame based on historical data and patterns.

Learn more: Learn to forecast trends and behaviors by mastering Predictive Analytics Courses.

Prescriptive analytics

Prescriptive analytics is a type of analytics that uses technology to analyze data for factors such as possible situations and scenarios, past and present performance, and other resources to help organizations make better strategic decisions.

Read more: Data Analysis Terms: A to Z Glossary


A prompt is an input that a user feeds to an AI system in order to get a desired result or output.

Quantum computing

Quantum computing is the process of using quantum-mechanical phenomena such as entanglement and superposition to perform calculations. Quantum machine learning uses these algorithms on quantum computers to expedite work because it performs much faster than a classic machine learning program and computer.

Reinforcement learning

Reinforcement learning is a type of machine learning in which an algorithm learns by interacting with its environment and then is either rewarded or penalized based on its actions.

Learn more: Dive into training models through rewards and penalties with Reinforcement Learning Courses.

Sentiment analysis

Also known as opinion mining, sentiment analysis is the process of using AI to analyze the tone and opinion of a given text.

Learn more: Learn how to interpret and analyze emotions in text data with Sentiment Analysis Courses.

Structured data

Structured data is data that is defined and searchable. This includes data like phone numbers, dates, and product SKUs.

Supervised learning

Supervised learning is a type of machine learning in which classified output data is used to train the machine and produce the correct algorithms. It is much more common than unsupervised learning.

Learn more: Develop skills in training models on labeled data with Supervised Learning Courses.


A token is a basic unit of text that an LLM uses to understand and generate language. A token may be an entire word or parts of a word.

Training data

Training data is the information or examples given to an AI system to enable it to learn, find patterns, and create new content.

Transfer learning

Transfer learning is a machine learning system that takes existing, previously learned data and applies it to new tasks and activities.

Turing test

The Turing test was created by computer scientist Alan Turing to evaluate a machine’s ability to exhibit intelligence equal to humans, especially in language and behavior. When facilitating the test, a human evaluator judges conversations between a human and machine. If the evaluator cannot distinguish between responses, then the machine passes the Turing test.

Unstructured data

Unstructured data is data that is undefined and difficult to search. This includes audio, photo, and video content. Most of the data in the world is unstructured.

Unsupervised learning

Unsupervised learning is a type of machine learning in which an algorithm is trained with unclassified and unlabeled data so that it acts without supervision.

Learn more: Explore the techniques for analyzing unlabeled data sets with Unsupervised Learning Courses.

Voice recognition

Voice recognition, also called speech recognition, is a method of human-computer interaction in which computers listen and interpret human dictation (speech) and produce written or spoken outputs. Examples include Apple’s Siri and Amazon’s Alexa, devices that enable hands-free requests and tasks.

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