Read the terms and their descriptions
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AI Vocabulary List for IT Professionals
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Artificial Intelligence (AI)
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, allowing computers to perform tasks such as learning, reasoning, problem-solving, and decision-making.
Machine learning is a type of artificial intelligence (AI) that uses algorithms to learn from data, identify patterns, and make decisions with minimal human intervention.
Deep learning is a type of machine learning that uses artificial neural networks with multiple layers to learn from and make decisions about data.
A neural network is a type of machine learning model inspired by the human brain, consisting of interconnected nodes organized in layers.
(in machine learning) a collection of data used to adjust the parameters of a computational model so as to optimize its performance in a task.
Test data is a collection of inputs used to validate software functionality, performance, and accuracy by simulating real-world scenarios and identifying bugs. I
A validation set is a subset of data used during the training phase of a machine learning model to tune its hyperparameters and prevent overfitting.
A dataset is a structured collection of related data that is treated as a single unit for analysis, processing, or storage.
In data science, a model is a mathematical or algorithmic framework trained on data to identify patterns, relationships, and trends.
An algorithm is a precise, step-by-step set of instructions or a finite sequence of well-defined rules designed to perform a specific task, solve a problem, or achieve a particular outcome.
Pattern recognition is the process of automatically identifying recurring arrangements of characteristics or regularities in data using computer algorithms.
A prediction is a statement or an educated estimate about an unknown event or an unknown value, often concerning the future.
In Artificial Intelligence (AI) and Machine Learning (ML), inference is the process of using a pre-trained model to make predictions, decisions, or generate outputs from new, unseen data.
Supervised learning is a method in artificial intelligence (AI) and machine learning (ML) where an algorithm learns from a labeled dataset to make predictions or decisions.
Unsupervised learning is a paradigm within artificial intelligence and machine learning that aims to find patterns, structures, and relationships within data without the use of predefined labels or target variables
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Reinforcement Learning
Reinforcement Learning (RL) is a paradigm within artificial intelligence where a software agent learns how to behave in an environment by performing actions and receiving rewards or penalties in return.
A feature in AI and machine learning refers to an individual, measurable property or characteristic of a phenomenon being observed.
Feature extraction is an essential process in artificial intelligence and machine learning that involves transforming raw data into a smaller set of features that still accurately represent the original information.
A label in AI and machine learning is the target variable or the output value that an algorithm is trying to predict, categorize, or forecast.
In artificial intelligence and machine learning, Input and Output refer to the data entering and exiting an AI system during its operation (inference) or training phases.
Ground truth in AI refers to the verified, accurate data or information from the real world used to train, validate, and test machine learning models.
Accuracy in AI and machine learning is an evaluation metric that measures the proportion of correct predictions made by a model out of the total number of predictions.
Precision in AI is an evaluation metric that measures the quality of a model's positive predictions.
Recall in AI, also known as sensitivity or the true positive rate, is a metric used to evaluate a model's ability to find all relevant instances in a dataset. It measures the proportion of actual positive cases that were correctly identified by the model.
The F1 Score is a metric in AI and machine learning that provides a single measure of a model's accuracy by combining both precision and recall. It is the harmonic mean of precision and recall.
Overfitting is a common and critical problem in AI and machine learning where a model learns the details and noise in the training data to the extent that it negatively impacts its performance on new, unseen data.
Underfitting is a problem in AI and machine learning that occurs when a model is too simple to capture the underlying patterns or relationships within the data.
Bias in AI refers to the systematic and unfair tendency of an artificial intelligence system to produce outcomes that disproportionately favor or disadvantage certain groups of people or outcomes.
Variance in AI and machine learning refers to the model’s sensitivity to small fluctuations or changes in the training data. It measures how much the model's predictions would change if a different subset of the training data were used.
Model generalization in AI and machine learning is the measure of how well a trained model is able to accurately predict outcomes or classify data it has never seen before, rather than just the data it was trained on.
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