ai glossary

Artificial Intelligence Glossary

Welcome to the world of Artificial Intelligence! Whether you're just starting your journey into this fascinating field or you're a seasoned professional, our comprehensive AI glossary is a valuable resource for you. From foundational concepts to advanced technologies, this glossary provides concise yet detailed descriptions of the terminologies and tools that are shaping the AI landscape today. We've compiled an A-to-Z list of terms, creating a one-stop reference guide for all things AI. Let's dive in and explore the intricate and exciting realm of artificial intelligence!

Table Of Contents


Artificial Intelligence (AI)

AI is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Ai Tools

Ai Tools refer to a variety of software and technologies used for creating, implementing, and managing artificial intelligence applications. These tools can include machine learning libraries, natural language processing APIs, data analytics platforms, and neural network frameworks. AI tools often enable developers to design systems capable of tasks such as pattern recognition, prediction, anomaly detection, decision-making, and language understanding.

AutoML (Automated Machine Learning)

AutoML refers to automated methods for applying machine learning to real-world problems. AutoML covers the complete pipeline from raw dataset to deployable machine learning models.



Backpropagation is a common method used to train deep neural networks and assign error values to each neuron. It works by calculating the gradient (the direction and rate of change) of the loss function and propagates it back through the network's layers.

Bayesian Network

A Bayesian network is a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian



Backpropagation is a common method used to train deep neural networks and assign error values to each neuron. It works by calculating the gradient (the direction and rate of change) of the loss function and propagates it back through the network's layers.

Bayesian Network

A Bayesian network is a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph.

BERT (Bidirectional Encoder Representations from Transformers)

BERT is a transformer-based machine learning technique for natural language processing (NLP) pre-training. It can understand the context of a word in a sentence by looking at the words that come before and after it.


Convolutional Neural Network (CNN)

A type of deep neural network often used for image recognition tasks, CNNs are designed to automatically and adaptively learn spatial hierarchies of features.


Chatbots are AI programs designed to simulate human conversation. They can be scripted or use more advanced machine learning techniques to provide more complex interactions.

Computer Vision

Computer vision is an interdisciplinary field that deals with how computers can gain high-level understanding from digital images or videos. It seeks to automate tasks that the human visual system can do.


Deep Learning

Deep learning is a subset of ML based on artificial neural networks with representation learning. It can learn from unstructured or unlabeled data, and typically involves a large number of layers for processing data.

Data Mining

Data mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the internet, and other information repositories.

Decision Tree

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences. It's one of the predictive modeling approaches used in statistics, data mining, and machine learning.


Evolutionary Algorithms

Evolutionary algorithms use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection to solve optimization problems.

Ensemble Learning

Ensemble learning uses multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Common types include bagging, boosting, and stacking.


Feature Engineering

Feature engineering is the process of selecting and transforming variables when creating a predictive model using machine learning.

Fuzzy Logic

Fuzzy Logic is a computational approach known for its basis in human reasoning, allowing for more advanced decision-making within AI systems.


Genetic Algorithm

A genetic algorithm is a search heuristic that is inspired by Charles Darwin's theory of natural evolution.

Gradient Descent

Gradient Descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient.



A heuristic is a practical approach to problem-solving where speed is prioritized over precision.


Hyperparameters are parameters that define the structure and behavior of an AI model. Unlike other parameters, they are set before the learning process begins.


Image Recognition

Image Recognition is the ability of an AI to identify objects, places, people, writing, and actions in images.

Inductive Learning

Inductive Learning is the process of learning by examples, where a system, from a set of observed instances, tries to induce a general rule.


Jupyter Notebook

Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

JSON (JavaScript Object Notation)

JSON is a lightweight data-interchange format that's easy for humans to read and write and easy for machines to parse and generate. It is often used when data is sent from a server to a web page.



Keras is a user-friendly neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.


K-means is an unsupervised machine learning algorithm used to classify items into k number of groups based on feature similarity.


Logistic Regression

Logistic Regression is a machine learning algorithm for classification problems. It is used to estimate discrete values (Binary values like 0/1, yes/no, true/false) based on a set of independent variables.

Linear Regression

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task, predicting the value of a goal variable based on other variables.


Machine Learning (ML)

Machine Learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

Monte Carlo Method

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to obtain numerical results.


Natural Language Processing (NLP)

NLP is a subfield of AI that focuses on how computers can understand and manipulate human language.

Neural Network

Neural Networks are a set of algorithms modeled after the human brain that are designed to recognize patterns.



Overfitting is a concept in machine learning where a statistical model describes random error or noise instead of the underlying relationship, leading to less accuracy in predictions.


In the context of computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations of the concepts, data, and entities that substantiate one, many, or all domains.



Python is a high-level programming language often used in artificial intelligence projects.


The perceptron is an algorithm for supervised learning of binary classifiers - functions that can decide whether an input belongs to one class or another.



Q-Learning is a values based algorithm in reinforcement learning. Q-learning is a model-free reinforcement learning algorithm to learn a policy telling an agent what action to take under what circumstances.

Quantum Computing

Quantum computing is a type of computation that uses quantum bits to do simultaneous calculations. It has potential implications for AI and machine learning, as it could significantly speed up processing times.


Reinforcement Learning

Reinforcement Learning is an aspect of machine learning where an agent learns to behave in an environment, by performing actions and seeing the results.

Random Forest

Random Forest is a machine learning algorithm that fits multiple decision trees to the data and uses averaging to improve the predictive accuracy and control overfitting.


Supervised Learning

Supervised Learning is a type of machine learning where the model learns from labeled data.

SVM (Support Vector Machine)

SVM is a supervised machine learning model that uses classification algorithms for two-group classification problems.



TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.

Transfer Learning

Transfer Learning is a machine learning method where a model developed for one task is reused as the starting point for a model on a second task.


Unsupervised Learning

Unsupervised Learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.


Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data.


Validation Set

A validation set is a set of examples used to tune the parameters of a classifier in supervised learning.


In the context of machine learning, a variable is any characteristic, number, or quantity that can be measured or counted.



In the context of machine learning, weights are the coefficients that the model assigns to different features in the data during training.

Word Embedding

Word Embedding is a type of word representation that allows words with similar meanings to have similar vector representations.



XGBoost stands for eXtreme Gradient Boosting, an open-source software library which provides a gradient boosting framework for languages such as Python, R, and Java.

XOR Problem

The XOR, or "exclusive or", problem is a classification problem in which the classes are linearly inseparable. It is a fundamental problem in the development of artificial neural networks.


YOLO (You Only Look Once)

YOLO is a real-time object detection system that identifies objects in a single glance, making it popular for applications that need to process video in real time.


A Yottabyte is a measure of data storage capacity and is 2 to the 80th power bytes, and is also equivalent to one septillion bytes.



A Z-score, or standard score, represents how many standard deviations an element is from the mean. In the context of machine learning, Z-score normalization is a common method to normalize data.

Zero-Shot Learning

Zero-Shot Learning refers to a machine's ability to recognize objects and concepts it has not been trained on, using knowledge of previously learned categories.