Artificial Intelligence (AI)

A branch of computer science aiming to create machines capable of intelligent behavior. [wiki]

Machine Learning (ML)

A subset of AI that enables machines to improve at tasks with experience. [wiki]

Deep Learning

An ML technique that teaches computers to learn by example, often using neural networks. [wiki]

Neural Networks

Algorithms modeled after the human brain, used in ML to recognize patterns. [wiki]

Supervised Learning

ML where the model is trained on labeled data. [wiki]

Unsupervised Learning

ML where the model learns from unlabeled data to find patterns. [wiki]

Reinforcement Learning

An area of ML where agents learn to make decisions by trial and error. [wiki]

Natural Language Processing (NLP)

AI that helps computers understand, interpret, and manipulate human language. [wiki]

Computer Vision

A field of AI that trains computers to interpret and understand the visual world. [wiki]

Convolutional Neural Network (CNN)

A deep learning algorithm often used in image recognition. [wiki]

Recurrent Neural Network (RNN)

A type of neural network well-suited for sequence data like speech. [wiki]

Generative Adversarial Network (GAN)

A network architecture for generative modeling using two networks against each other. [wiki]

Transfer Learning

Applying knowledge gained in one problem to a different but related problem. [wiki]

Data Mining

The process of discovering patterns and relationships in large datasets. [wiki]

Predictive Analytics

Using data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes. [wiki]


A set of rules or instructions given to an AI to help it learn or make decisions. [wiki]

Bias in AI

Prejudiced outcomes due to assumptions in the machine learning process. [wiki]


A software application used to conduct an online chat conversation via text or text-to-speech. [wiki]


A type of supervised learning that categorizes data into labels. [wiki]


A type of unsupervised learning that groups unlabelled data based on similarities. [wiki]

Decision Tree

A model for decision-making used in non-linear predictions. [wiki]

Dimensionality Reduction

The process of reducing the number of random variables under consideration. [wiki]

Ensemble Learning

Techniques that combine several base models to improve performance. [wiki]

Feature Engineering

The process of selecting, modifying, and creating features to improve ML models. [wiki]

Gradient Descent

An optimization algorithm for finding the minimum of a function. [wiki]


A practical approach to problem-solving that isn't perfect but is sufficient for immediate goals. [wiki]

Hyperparameter Tuning

The process of selecting the set of optimal parameters for a learning algorithm. [wiki]


The process of using a trained model to make predictions. [wiki]

IoT (Internet of Things)

The network of physical objects embedded with sensors, software, and other technologies for data connectivity. [wiki]

K-means Clustering

A popular method of clustering used in data mining. [wiki]

Logistic Regression

A statistical method for predicting binary classes. [wiki]


In AI, an output of the training process which can be used for making predictions. [wiki]

Natural Language Understanding (NLU)

A subset of NLP focused on machine reading comprehension. [wiki]


An ML model that is too closely fitted to a specific dataset and may fail to generalize. [wiki]


A metric used in classification to evaluate the accuracy of positive predictions. [wiki]

Quantum Computing

A type of computing using quantum-mechanical phenomena such as superposition and entanglement. [wiki]

Random Forest

An ensemble learning method based on decision tree algorithms. [wiki]

Regression Analysis

A statistical process for estimating relationships among variables. [wiki]

Sentiment Analysis

The process of computationally determining whether a piece of writing is positive, negative, or neutral. [wiki]

Support Vector Machine (SVM)

A supervised learning model used for classification and regression analysis. [wiki]


An open-source software library for high-performance numerical computation, particularly well-suited for deep learning applications. [wiki]

Time Series Analysis

A method of analyzing a series of data points ordered in time. [wiki]


A model that is too simple, both in terms of the structure of the model and the number of parameters. [wiki]

Unsupervised Learning

ML using information that is neither classified nor labeled. [wiki]


The process of evaluating a trained ML model with a new data set. [wiki]


A scalable and accurate implementation of gradient boosting machines. [wiki]

YOLO (You Only Look Once)

A real-time object detection system. [wiki]

Zero-shot Learning

The ability of a machine to recognize objects it has never seen before. [wiki]

Activation Function

A function in a neural network that introduces non-linear properties to the model. [wiki]


A method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data is processed. [wiki]