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AI Glossary

Welcome to our beginner-friendly glossary of Artificial Intelligence (AI) terms. This resource is designed to help you familiarize yourself with key AI concepts, making  machine learning and intelligent systems more accessible and understandable.

Glossary Terms


An algorithm in the context of AI and machine learning is a set of rules or instructions given to an AI program, neural network, or other machine to help it learn on its own. Algorithms dictate the process of data analysis, pattern recognition, and decision-making based on data inputs.

Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI can be categorized as either weak or strong, where weak AI is designed and trained for a specific task, while strong AI carries out tasks that are more complex and human-like. Example: Siri, Apple’s virtual assistant, uses AI to understand and process user requests.

Artificial Neural Network (ANN)

A computational model inspired by the way neural networks in the human brain process information. ANNs are used in deep learning and can recognize patterns and make predictions. For instance, ANNs are used in facial recognition software.


Refers to machines or systems capable of performing tasks without human intervention. An example is an autonomous vehicle that can drive itself.


Backpropagation is an algorithm commonly used to train neural networks. During training, the backpropagation algorithm calculates and assigns responsibility for errors in the output, then adjusts the weights and biases in the network in a way that minimizes these errors. Example: In training a neural network to recognize handwritten digits, backpropagation adjusts the model weights to improve accuracy.

Bayesian Network

A probabilistic model representing a set of variables and their conditional dependencies using a directed acyclic graph. It is used for risk assessment, prediction, and decision making.

Big Data

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Big Data is essential for training complex AI models.

Binary Classification

A type of classification task in AI where an input is classified into one of two possible categories. For example, an email spam filter classifying emails as ‘spam’ or ‘not spam’.


A software application that performs automated, repetitive, pre-defined tasks. Bots are often used in chat interfaces to answer common questions – like customer service chatbots.


A specific type of bot designed to simulate human conversation. These can range from simple, rule-based systems to advanced systems powered by machine learning.


Classification in machine learning is a type of supervised learning technique where the aim is to categorize data into predefined classes. The model learns from the training data and then uses this learning to classify new observations. Example: An email spam filter is a classification tool that categorizes emails as ‘spam’ or ‘not spam.’


A type of unsupervised learning where AI algorithms group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. It’s used in market segmentation.

Computer Vision

A field of AI that trains computers to interpret and understand the visual world. For example, using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects.

Convolutional Neural Network (CNN)

A Convolutional Neural Network (CNN) is a deep learning algorithm that can take an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and differentiate one from the other. CNNs are primarily used in image processing, computer vision, and image recognition. Example: CNNs are used in face recognition systems.

Data Mining

Data Mining is the process of discovering patterns, correlations, and anomalies within large sets of data with the goal of predicting outcomes. It combines machine learning, statistics, and database systems. Example: Companies use data mining to analyze customer data and develop more effective marketing strategies.

Data Science

A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It overlaps with AI but also includes other techniques.

Deep Learning

Deep Learning is a subset of machine learning in AI that has networks capable of learning unsupervised from data that is unstructured or unlabeled. It uses algorithms known as neural networks designed to mimic human decision-making patterns. Example: Deep learning is used in self-driving cars to identify objects like pedestrians, traffic lights, and other vehicles.

Decision Tree

A decision support tool that uses a tree-like model of decisions and their possible consequences. It’s used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.

Dimensionality Reduction

The process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It’s often used in data preprocessing to reduce the complexity of data and avoid overfitting.

Ensemble Learning

Ensemble learning is a technique in machine learning where multiple models, often called “weak learners,” are combined to solve the same problem. This is done with the goal of improving the overall performance and accuracy of the model. The combined models work together to produce a more robust and accurate output than any single model could on its own. Example: A common example of ensemble learning is the Random Forest algorithm, which combines multiple decision trees to make more accurate predictions than any single tree could.


An epoch in machine learning refers to one complete cycle through the entire training dataset. During an epoch, the machine learning model’s internal parameters are adjusted in response to the training data. Multiple epochs are often required for the model to effectively learn from the data. Example: When training a neural network for image classification, you might set the training process to run for 50 epochs.

Evolutionary Algorithms

Evolutionary algorithms are optimization algorithms based on the principles of biological evolution, such as reproduction, mutation, recombination, and selection. These algorithms iteratively evolve a set of candidate solutions towards an optimal solution. Example: An evolutionary algorithm could be used to optimize the aerodynamic design of a car, where each iteration represents a “generation” of design variations.


In machine learning, a feature is an individual measurable property or characteristic of a phenomenon being observed. Features are used as input variables in machine learning models. Selecting the right features is critical for the performance of these models. Example: In a dataset used for predicting house prices, features might include the number of bedrooms, the size of the house in square feet, and the age of the house.

Feature Engineering

Feature engineering is the process of using domain knowledge to create or transform raw data into features that make machine learning algorithms work more effectively. It is a critical step in the data preparation process. Example: In text classification, converting raw text into features like word counts or term frequencies.

Feature Selection

Feature selection involves choosing the most relevant variables for use in a model. By reducing the number of input variables, it helps to simplify models, reduce overfitting, and improve performance. Example: In a health-related dataset, selecting key features like age, weight, and blood pressure, while ignoring irrelevant features like patient ID number.

Federated Learning

Federated learning is a machine learning approach where a model is trained across multiple decentralized devices or servers with local data samples, without exchanging the data. This method is beneficial for privacy preservation and reducing data transmission requirements. Example: Improving a predictive text model across multiple smartphones without sharing individual typing data with a central server.

GAN (Generative Adversarial Network)

Generative Adversarial Networks are a class of artificial intelligence algorithms used in unsupervised machine learning. They involve two neural networks, a generator and a discriminator, which compete against each other, improving their methods in the process. Example: Creating realistic synthetic images that can be used for training computer vision systems without the need for real-world data.

Gradient Descent

Gradient descent is an optimization algorithm used for minimizing the cost function in machine learning and deep learning algorithms. It involves iteratively adjusting the parameters of a model to find the minimum value of the function. Example: Optimizing the weights in a linear regression model to minimize the difference between the predicted and actual values.


A heuristic is a practical approach to problem-solving that employs a readily accessible, but not necessarily optimal, solution. In AI, heuristics are used for decision-making, especially in algorithms that involve search or optimization. Example: In a pathfinding algorithm like A*, heuristics might be used to estimate the shortest path to the goal.

Hidden Layer

In an artificial neural network, a hidden layer is a layer of nodes (neurons) between the input layer and the output layer. These layers are where the network processes inputs and makes computations based on the weights and biases of the neurons. Example: In a deep learning model for image recognition, hidden layers are responsible for detecting edges, shapes, and other features in the input images.


Hyperparameters are the configuration settings used to structure machine learning models. These parameters are set prior to training and influence the behavior and performance of the model. Example: In a neural network, the learning rate and the number of hidden layers are hyperparameters that can affect how well the model learns from data.


In the context of machine learning, inference refers to the process of using a trained model to make predictions or decisions based on new, unseen data. This is the stage where the model, after being trained and validated, is actually put to use. Example: Using a trained image classification model to classify new images into predefined categories.

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. It’s widely used in data science and machine learning for exploratory data analysis, data cleaning, statistical modeling, and visualization. Example: A data scientist might use a Jupyter Notebook to experiment with different machine learning models on a dataset.

K-Means Clustering

K-means clustering is an unsupervised machine learning algorithm used for clustering data into a predefined number of groups, known as ‘k’. It’s used when you have unlabeled data and you want to categorize it into distinct groups based on similarity. Example: Segmenting customers into different groups based on purchasing behavior for targeted marketing.

Linear Regression

Linear regression is a basic and commonly used type of predictive analysis in statistics and machine learning. The overall idea is to find a relationship between two or more features. In simple terms, it predicts a response (dependent variable) based on a single feature (independent variable). Example: Predicting house prices based on their size (square footage).

Loss Function

A loss function, also known as a cost function, is a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your loss function will output a higher number. Minimizing this function is key in training efficient and accurate models. Example: Mean Squared Error (MSE) is a common loss function used in regression models to measure the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.

Logistic Regression

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a binary variable (where there are only two possible outcomes). It’s used extensively in fields like medical and social sciences. Example: Determining whether a patient has a certain disease (yes or no) based on their age, weight, and blood pressure.

Long Short-Term Memory (LSTM)

LSTMs are a special kind of Recurrent Neural Network (RNN), capable of learning long-term dependencies. They are used in deep learning because traditional RNNs struggle with backpropagation when sequences are long. Example: LSTMs are widely used in sequence prediction problems, such as predicting the next word in a sentence.

Machine Learning

Machine Learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves. Example: A machine learning model can be trained to recognize patterns in data, such as identifying fraudulent credit card transactions.

Model Tuning

Model tuning refers to the process of adjusting the parameters of a machine learning model to improve its performance. This often involves finding the optimal values for hyperparameters through methods like grid search or random search. Example: Tuning the hyperparameters of a Random Forest model, such as the number of trees or the depth of each tree, to improve classification accuracy.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of AI that gives machines the ability to read, understand, and derive meaning from human languages. It combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Example: Chatbots and virtual assistants like Siri use NLP to interpret and respond to human speech.

Neural Network

A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. Example: Image recognition applications use neural networks to identify and categorize images.


Overfitting occurs in machine learning when a model is excessively complex and learns both the noise and the signal in the training dataset. As a result, it performs well on the training data but poorly on new, unseen data. This is often a result of a model being too complex. Example: A model that is trained to perfectly memorize the stock prices for the past year, but fails to predict future prices accurately.

O – Outlier

In statistics and data analysis, an outlier is an observation point that is distant from other observations. In the context of machine learning, outliers can significantly skew results. Handling outliers is a key step in data preprocessing. Example: In a dataset of house prices, a house that is priced significantly higher or lower than the rest of the houses can be an outlier.


Precision in machine learning refers to the ratio of true positives to the total number of positive predictions. It measures the accuracy of the positive predictions. Example: In a spam detection model, precision would be the proportion of emails correctly identified as spam out of all the emails identified as spam.


Q-Learning is a type of reinforcement learning algorithm that aims to learn the value of an action in a particular state. It doesn’t require a model of the environment and can handle problems with stochastic transitions and rewards. Example: Q-Learning can be used to train an agent to play games like chess or Go, where the algorithm learns the best moves to make in different board positions.

Random Forest

Random Forest is an ensemble learning method used for classification and regression. It operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes (for classification) or mean prediction (for regression) of the individual trees. Example: A random forest model could be used to predict whether a loan applicant is a high or low credit risk based on features like income, employment history, and credit score.


Regression is a type of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). It’s used for forecasting, time series modeling, and finding the causal effect relationship between variables. Example: Linear regression could be used to predict house prices based on various features like size, location, and age of the house.

Reinforcement Learning

Reinforcement Learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. The agent learns by trial and error, receiving rewards or penalties for the actions it performs. Example: Training a robot to navigate through a maze where it receives rewards for reaching the end and penalties for hitting walls.

Supervised Learning

Supervised learning is a type of machine learning algorithm that uses a known dataset (known as the training dataset) to make predictions. The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. Example: A supervised learning model could be trained to classify emails as either spam or not spam based on known examples of each.

Support Vector Machine (SVM)

Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. It performs classification by finding the hyperplane that best differentiates the two classes. Example: SVMs are used for handwriting recognition, where the algorithm classifies segments of handwriting as different letters.


In the context of machine learning, a tensor is a generalization of vectors and matrices and is easily understood as a multi-dimensional array. In many machine learning frameworks, tensors are used as a fundamental data structure. Example: In deep learning frameworks like TensorFlow, tensors are used to represent the data that is fed into and processed by neural networks.

Training Dataset

A training dataset is a collection of data used to train a machine learning model. It includes both input data and the corresponding correct output, and it is through this dataset that the model learns to make predictions or classify data. Example: In a model for recognizing handwritten digits, the training dataset would consist of images of handwritten digits and their corresponding labels.

Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is used to find hidden patterns or intrinsic structures in input data. Example: Clustering is a common unsupervised learning technique, used, for example, in customer segmentation.

Validation Dataset

A validation dataset is used to provide an unbiased evaluation of a model fit during the training phase. It is used to fine-tune model parameters, typically by providing a metric for model performance while avoiding overfitting to the training dataset. Example: In a predictive modeling task, a validation dataset helps in deciding which of several models might perform best on unseen data.


In the context of machine learning, variance refers to the extent to which a model’s predictions vary for a given dataset. High variance can cause an algorithm to model the random noise in the training data, leading to overfitting. Example: A complex decision tree might have high variance, fitting the training data very closely, but failing to generalize well to new data.


In machine learning, particularly in neural networks, weights are used to adjust the strength of the influence of one unit (a neuron or an input) on another. The process of learning in neural networks involves adjusting these weights based on the error of the output compared to the expected result. Example: In a neural network used for image recognition, weights are adjusted during training to improve the accuracy of the model in identifying images.


XGBoost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees designed for speed and performance. It is widely used in machine learning competitions and practical applications. Example: XGBoost has been used effectively in many Kaggle competitions for tasks like classification and regression.

YOLO (You Only Look Once)

YOLO (You Only Look Once) is a state-of-the-art, real-time object detection system. It is known for its speed and accuracy in detecting objects in images and videos. Unlike other detection systems which first propose regions and then classify each region separately, YOLO applies a single neural network to the full image, dividing the image into regions and predicting bounding boxes and probabilities for each region simultaneously. This approach allows YOLO to detect objects with high precision in real-time. Example: YOLO can be used in autonomous vehicles to detect objects like pedestrians, cars, traffic lights, and so on, in real-time, aiding in navigation and safety.


In statistics, a Z-score is a numerical measurement that describes a value’s relationship to the mean of a group of values, measured in terms of standard deviations from the mean. In machine learning, it is often used in normalization of data. Example: Standardizing the features in a dataset so that they have a mean of 0 and a standard deviation of 1, which can be important for models sensitive to the scale of input features, like SVM.

Transfer Learning

Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It’s a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. Example: Using a model pre-trained on a large dataset of images (like ImageNet) to start a model that can classify types of flowers, without needing the same amount of data for training as the initial model.

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