Artificial Intelligence (AI)
The simulation of human intelligence by machines, enabling them to perform tasks like reasoning, learning, and problem-solving.
Machine Learning (ML)
A subset of AI that enables systems to learn and improve from experience without explicit programming.
Deep Learning
A type of ML using neural networks with multiple layers to analyze complex data patterns.
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process information.
Natural Language Processing (NLP)
The ability of machines to understand, interpret, and respond to human language.
Supervised Learning
A machine learning approach where models are trained on labeled datasets to make predictions.
Unsupervised Learning
An ML approach where models find patterns in unlabeled data without specific outcomes provided.
Reinforcement Learning
A method where an agent learns by interacting with an environment to maximize rewards.
Big Data
Extremely large datasets that require advanced methods to analyze for actionable insights.
Data Mining
The process of discovering patterns and relationships in large datasets.
Algorithm
A step-by-step procedure or formula for solving a problem.
Predictive Analytics
Using data, statistical algorithms, and ML to predict future outcomes.
Cognitive Computing
Simulating human thought processes in a computerized model, often used in decision-making.
Computer Vision
A field of AI focused on enabling machines to interpret and analyze visual information.
Chatbot
An AI-powered program that interacts with users through text or voice.
AI Ethics
The study of moral issues surrounding the development and use of AI technologies.
Explainable AI (XAI)
AI systems designed to provide clear, understandable explanations of their decisions.
Feature Engineering
The process of selecting, modifying, or creating data features to improve ML model performance.
Training Data
The dataset used to teach a machine learning model during its development.
Test Data
A dataset used to evaluate the performance of an ML model after training.
Hyperparameters
Settings used to control the behavior of ML models, like learning rate and batch size.
Overfitting
A modeling error where an ML model performs well on training data but poorly on unseen data.
Underfitting
When a model is too simple and fails to capture the data’s underlying patterns.
Artificial Neural Networks (ANNs)
A type of neural network commonly used in deep learning applications.
Edge Computing
Performing data processing near the data source, reducing latency and improving efficiency.
Generative AI
AI systems that create new content, such as images, text, or music.
Turing Test
A test to determine whether a machine’s behavior is indistinguishable from that of a human.
Bias in AI
Systematic errors in AI systems caused by biases in the training data.
Automation
Using technology to perform tasks with minimal human intervention.
Robotic Process Automation (RPA)
Using software bots to automate repetitive tasks, often in business processes.