AI Glosarry
A/B testing, also known as split testing, is a method used to compare two versions of a webpage or other digital asset to determine which one performs better Read more.
Action space refers to the set of all possible actions that an agent can take in a given environment Read more.
Activation, in the context of neural networks and machine learning, refers to the process of calculating the output of a neuron or a layer in a neural network Read more.
Adaptive learning refers to a teaching and learning approach that utilizes technology and data-driven techniques to personalize the learning experience for individual learners Read more.
Adaptive learning refers to a teaching and learning approach that utilizes technology and data-driven techniques to personalize the learning experience for individual learners Read more.
Adversarial examples refer to specially crafted inputs that are intentionally designed to deceive or mislead machine learning models Read more.
An algorithm is a step-by-step procedure or set of rules for solving a specific problem or performing a specific task Read more.
Annotated data, also known as labeled data, refers to data that has been manually tagged or annotated with relevant information or labels. Annotations are added to the data to provide additional context, categorization, or semantic meaning to aid in the training or evaluation of machine learning models Read more.
Anomaly detection, also known as outlier detection, is a technique used to identify patterns or instances in data that deviate significantly from the norm or expected behavior Read more.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to perform tasks that would typically require human intelligence Read more.
The attention mechanism is a key component in many modern deep learning models, particularly in the field of natural language processing (NLP) Read more.
The AUC-ROC curve, often referred to as the ROC curve, is a graphical representation of the performance of a binary classification model Read more.
An autoencoder is an unsupervised learning algorithm used for dimensionality reduction and data reconstruction Read more.
Automated Machine Learning (AutoML) refers to the use of automated tools and techniques to automate various steps in the machine learning workflow, including data preparation, feature engineering, model selection, hyperparameter tuning, and model evaluation Read more.
Automated reporting refers to the process of automatically generating reports, summaries, or visualizations from data without manual intervention Read more.
Backpropagation is a fundamental algorithm used in training artificial neural networks (ANNs) through supervised learning Read more.
The Bag of Words (BoW) is a popular and simple technique used in natural language processing (NLP) and information retrieval to represent text data Read more.
Batch normalization is a technique commonly used in deep neural networks to improve training efficiency and stability Read more.
Bayesian inference is a framework for updating and revising probabilities based on new evidence and prior knowledge Read more.
Bayesian inference is a framework for updating and revising probabilities based on new evidence and prior knowledge Read more.
Bayesian optimization is a sequential model-based optimization technique that aims to find the optimal configuration or set of hyperparameters for a given objective function, which can be time-consuming or expensive to evaluate Read more.
Bias correction refers to the process of adjusting or eliminating bias in data or statistical analyses to obtain more accurate and reliable results Read more.
The bias-variance tradeoff is a fundamental concept in machine learning that involves finding the right balance between bias and variance when building predictive models Read more.
Big data analytics refers to the process of examining large and complex datasets, known as big data, to uncover patterns, correlations, and insights that can inform decision-making, improve business processes, and drive innovation
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A Boltzmann machine is a type of generative stochastic artificial neural network that uses a set of binary units or nodes to model complex probability distributions Read more.
Bootstrapping is a statistical resampling technique that involves repeatedly sampling from a dataset with replacement to obtain additional datasets
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Built-in intelligence refers to the incorporation of artificial intelligence (AI) capabilities and algorithms directly into software or hardware systems Read more.
Backpropagation is a fundamental algorithm used in training artificial neural networks (ANNs) through supervised learning Read more.
A Cascade Classifier, also known as a Cascade of Classifiers, is a machine-learning algorithm commonly used for object detection and recognition Read more.
Causal inference is a field of statistics and research that aims to understand and establish causal relationships between variables or events Read more.
Categorical data, also known as qualitative or discrete data, represent variables that take on values from a specific set of categories or groups Read more.
Character-level language modeling is an approach to natural language processing (NLP) and text generation that operates at the level of individual characters Read more.
The chi-square test is a statistical test used to determine if there is a significant association between two categorical variables Read more.
Class imbalance refers to a situation in a classification problem where the distribution of classes in the training data is heavily skewed Read more.
Cloud computing refers to the delivery of computing services, including servers, storage, databases, networking, software, and more, over the Internet Read more.
Cluster analysis is a statistical technique used to group similar objects or data points into clusters or subgroups Read more.
Clustering is an unsupervised machine learning technique that involves grouping similar objects or data points together based on their inherent similarities Read more.
The coefficient of determination, often referred to as R-squared (R²), is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s) in a regression model Read more.
Collaborative filtering is a technique used in recommendation systems to predict user preferences or interests by collecting and analyzing information from the behavior and preferences of a group of users Read more.
Collinearity, also known as multicollinearity, refers to a high correlation or linear relationship between two or more predictor variables in a regression model Read more.
Combinatorial optimization refers to the problem of finding the best possible solution among a finite set of possibilities, where the set of possibilities is typically large and discrete Read more.
The Common Data Model (CDM) is a standardized and extensible data schema and semantic model that is designed to enable interoperability and consistency of data across different applications and services Read more.
The Common Data Model (CDM) is a standardized and extensible data schema and semantic model that is designed to enable interoperability and consistency of data across different applications and services Read more.
Conditional Adversarial Networks (CANs) are a variant of generative adversarial networks (GANs) that incorporate conditional information during the training process Read more.
A confidence interval is a range of values that is calculated from sample data and is used to estimate an unknown population parameter with a certain level of confidence Read more.
A confidence interval is a range of values that is calculated from sample data and is used to estimate an unknown population parameter with a certain level of confidence Read more.
In various contexts, “convergence" refers to the process or state of approaching a common value, point, or condition Read more.
Convolution is a fundamental operation used in a wide range of applications, including mathematics, signal processing, image processing, and deep learning Read more.
A convolutional autoencoder is a type of autoencoder that incorporates convolutional layers in its architecture Read more.
A Convolutional Neural Network (CNN) is a type of deep learning model designed specifically for processing structured grid-like data, such as images, video, and audio Read more.
A covariance matrix is a square matrix that summarizes the covariances between multiple variables in a dataset Read more.
Cross-entropy loss, also known as log loss or logistic loss, is a common loss function used in classification tasks, particularly in machine learning models that employ logistic regression or softmax activation Read more.
Cross-validation is a resampling technique used in machine learning and statistics to assess the performance and generalization ability of a predictive model Read more.
Customer segmentation is the process of dividing a customer base into distinct groups or segments based on specific characteristics, behaviors, or needs Read more.
Data augmentation is a technique used in machine learning and data analysis to increase the size and diversity of a dataset by creating new synthetic data points from existing data Read more.
Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets Read more.
Data engineering refers to the practice of designing, constructing, and maintaining the infrastructure and systems that enable the collection, storage, processing, and delivery of data in a reliable, efficient, and scalable manner Read more.
Data exploration, also known as exploratory data analysis (EDA), is the process of examining and understanding the characteristics, patterns, and relationships within a dataset Read more.
Data imputation, also known as missing data imputation, is the process of estimating or filling in missing values in a dataset. Missing data can occur due to various reasons such as data collection errors, equipment malfunctions, survey non-responses, or data corruption Read more.
Data leakage refers to the unintentional or unauthorized disclosure of sensitive or confidential data to unintended parties or systems Read more.
Data mining is the process of discovering patterns, relationships, and insights from large volumes of data Read more.
Data normalization, also known as data standardization or feature scaling, is a preprocessing technique used to transform numerical data into a common scale or range Read more.
Data preprocessing is an essential step in data analysis and machine learning that involves transforming raw data into a format suitable for further analysis and modeling Read more.
Data science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data Read more.
Data visualization is the representation of data in a visual or graphical format to facilitate understanding, exploration, and communication of information Read more.
Data wrangling, also known as data munging or data preprocessing, refers to the process of transforming and cleaning raw data into a format that is suitable for analysis or further processing Read more.
A decision boundary is a conceptual boundary or surface that separates different classes or categories in a classification problem Read more.
Decision forests, also known as random forests, are an ensemble learning method used for both classification and regression tasks Read more.
A decision stump, also known as a one-level decision tree, is a simple machine-learning model used for binary classification Read more.
A decision tree is a popular and versatile machine learning algorithm used for both classification and regression tasks Read more.
Deep Belief Networks (DBNs) are a type of artificial neural network that consists of multiple layers of interconnected nodes, with connections between nodes occurring only between adjacent layers Read more.
Deep learning is a subfield of machine learning that focuses on the development and application of artificial neural networks with multiple layers, known as deep neural networks Read more.
Deep Q-Network (DQN) is a reinforcement learning algorithm that combines deep learning with Q-learning to solve complex sequential decision-making problems Read more.
Deep reinforcement learning (DRL) is a subfield of machine learning that combines deep learning techniques with reinforcement learning to enable agents to learn and make decisions in complex environments Read more.
Differential privacy is a framework and set of techniques aimed at preserving the privacy of individuals while allowing for the analysis of sensitive data Read more.
Digital transformation refers to the process of integrating digital technologies into all aspects of an organization’s operations, strategies, and business models to fundamentally change how it operates and delivers value to customers Read more.
Digital twins refer to virtual replicas or digital representations of physical objects, processes, or systems Read more.
The “curse of dimensionality" refers to the challenges and problems that arise when working with high-dimensional data Read more.
Dimensionality reduction is the process of reducing the number of variables or features in a dataset while retaining as much relevant information as possible Read more.
Dropout regularization is a technique commonly used in deep learning to mitigate overfitting in neural networks Read more.
Early stopping is a technique used in machine learning to prevent overfitting and improve the generalization performance of a model during training Read more.
Elastic Net regularization is a technique used in machine learning to address the limitations of L1 (Lasso) and L2 (Ridge) regularization methods Read more.
The embedding layer is a fundamental component in many natural language processing (NLP) and deep learning models Read more.
Ensemble learning is a machine learning technique that involves combining the predictions of multiple individual models to improve the overall performance and predictive accuracy Read more.
Ensemble methods are machine learning techniques that combine multiple models, often referred to as base models or weak learners, to make more accurate and robust predictions Read more.
Ensemble voting is a popular technique in ensemble learning where the final prediction is made by combining the predictions of multiple individual models Read more.
Error analysis is a systematic process of examining and understanding the errors made by a machine learning model Read more.
An error function, also known as a loss function or objective function, is a mathematical function that quantifies the discrepancy between the predicted output of a machine learning model and the true or desired output Read more.
Ethics in AI refers to the study and application of moral principles and values in the development, deployment, and use of artificial intelligence systems Read more.
ETL, which stands for Extract, Transform, Load, is a process used in data integration and data warehousing to gather data from multiple sources, transform it into a consistent format, and load it into a target system such as a database or a data warehouse Read more.
Evaluation metrics are used to measure the performance and effectiveness of machine learning models or algorithms Read more.
Evolutionary computation (EC) is a family of computational techniques inspired by biological evolution and natural selection Read more.
Explainable AI (XAI) refers to the development and deployment of artificial intelligence (AI) systems that can provide clear and understandable explanations of their decisions or predictions to humans Read more.
Explainable AI (XAI) refers to the development and deployment of artificial intelligence (AI) systems that can provide clear and understandable explanations of their decisions or predictions to humans Read more.
Exploratory Factor Analysis (EFA) is a statistical technique used to uncover the underlying structure or latent factors in a dataset Read more.
Factor analysis is a statistical technique used to uncover underlying factors or dimensions within a set of observed variables Read more.
The False Discovery Rate (FDR) is a statistical concept and method used in multiple testing problems, particularly in hypothesis testing when conducting multiple comparisons simultaneously Read more.
False negatives are errors that occur in hypothesis testing or diagnostic testing when the test fails to detect a true positive result Read more.
False positives are errors that occur in hypothesis testing or diagnostic testing when the test incorrectly indicates the presence of a condition or attribute when it is actually absent Read more.
Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models Read more.
Feature extraction is a process in machine learning and data analysis that involves transforming raw data into a new set of features or representations that capture the essential information needed for a specific task Read more.
Feature scaling is a preprocessing technique used in machine learning to standardize or normalize the range of features or variables in a dataset Read more.
A feedforward neural network, also known as a multilayer perceptron (MLP), is a type of artificial neural network where information flows in one direction, from the input layer through one or more hidden layers to the output layer Read more.
Fine-tuning refers to the process of taking a pre-trained machine learning model and further training it on a specific task or dataset to improve its performance Read more.
Forecasting is the process of making predictions or estimates about future events, outcomes, or trends based on historical data and statistical techniques Read more.
Forward propagation, also known as forward pass, is a fundamental process in neural networks where the input data is passed through the network’s layers to generate predictions or outputs Read more.
Fraud detection is a critical application of data analysis and machine learning techniques to identify and prevent fraudulent activities or behaviors Read more.
Frequent pattern mining is a data mining technique that aims to discover patterns or associations that occur frequently in a dataset Read more.
A fully connected neural network, also known as a dense or feedforward neural network, is a type of artificial neural network where each neuron in one layer is connected to every neuron in the subsequent layer Read more.
A fully connected neural network, also known as a dense or feedforward neural network, is a type of artificial neural network where each neuron in one layer is connected to every neuron in the subsequent layer Read more.
A Gaussian Mixture Model (GMM) is a probabilistic model that represents the probability distribution of a dataset as a mixture of Gaussian (normal) distributions Read more.
Gaussian processes (GPs) are probabilistic models that define a distribution over functions Read more.
Generative Adversarial Networks (GANs) are a class of deep learning models that consist of two neural networks, a generator and a discriminator, that are trained in an adversarial manner Read more.
A generative model is a type of statistical model that learns and represents the underlying probability distribution of a dataset Read more.
Genetic Algorithms (GAs) are search and optimization algorithms inspired by the process of natural selection and genetics Read more.
Gradient Boosting Machine (GBM) is a machine learning technique that combines multiple weak learners (usually decision trees) to create a strong predictive model Read more.
Gradient Boosting Machine (GBM) is a machine learning technique that combines multiple weak learners (usually decision trees) to create a strong predictive model Read more.
Gradient descent optimization is an iterative optimization algorithm commonly used in machine learning and mathematical optimization Read more.
Heatmaps are graphical representations of data where values are encoded as colors on a two-dimensional grid Read more.
The hashing trick is a technique used in machine learning and natural language processing (NLP) to efficiently represent categorical or text features as fixed-length vectors Read more.
Hierarchical clustering is a popular unsupervised learning technique used to group similar data points into clusters based on their proximity or similarity Read more.
Hierarchical Reinforcement Learning (HRL) is an approach in the field of reinforcement learning that aims to tackle complex tasks by decomposing them into a hierarchy of subtasks or skills Read more.
Hinge loss is a loss function commonly used in machine learning, particularly in the context of support vector machines (SVMs) for classification tasks Read more.
Hybrid learning, also known as blended learning, refers to an educational approach that combines traditional face-to-face instruction with online or digital learning methods Read more.
Hyperparameters are parameters that are not learned from the data but are set by the user or data scientist before the training of a machine learning model. Read more.
Hyperparameter optimization, also known as hyperparameter tuning, is the process of finding the best set of hyperparameters for a machine learning model Read more.
Hyperparameter tuning, also known as hyperparameter optimization, is the process of finding the best combination of hyperparameter values for a machine learning model Read more.
Hypothesis testing is a statistical procedure used to make inferences or draw conclusions about a population based on sample data Read more.
Image classification is a computer vision task that involves categorizing images into predefined classes or categories Read more.
Image recognition, also known as object recognition or visual recognition, is a computer vision task that involves identifying and labeling specific objects or patterns within an image or a video Read more.
Image segmentation is a computer vision task that involves dividing an image into multiple regions or segments to extract meaningful information and separate objects or regions of interest from the background Read more.
Imbalanced classes refer to a situation in which the distribution of class labels in a dataset is heavily skewed, with one or more classes being significantly underrepresented compared to others Read more.
Imprecision refers to a lack of accuracy, precision, or specificity in the results or measurements obtained from a process, system, or model Read more.
Imputation is a technique used to fill in missing or incomplete values in a dataset. Missing data can occur for various reasons, such as data collection errors, measurement failures, or participants choosing not to provide certain information Read more.
Independent Component Analysis (ICA) is a computational method used to separate a set of observed signals into statistically independent components Read more.
Inductive bias refers to the set of assumptions, beliefs, or constraints that guide the learning process of a machine learning algorithm or a human learner Read more.
Inductive learning, also known as inductive reasoning or inductive inference, is a type of learning that involves generalizing from specific instances or examples to make broader generalizations or predictions Read more.
Inductive Logic Programming (ILP) is a subfield of machine learning that combines elements of logic programming and inductive reasoning Read more.
An inference engine is a component of an artificial intelligence (AI) system that is responsible for reasoning and making logical deductions based on the available knowledge or information Read more.
Information gain is a measure used in decision tree algorithms and other machine learning algorithms to assess the relevance or importance of a feature in predicting or classifying a target variable Read more.
Instance segmentation is a computer vision task that involves detecting and delineating individual objects within an image at the pixel level Read more.
Instance-based learning, also known as lazy learning or memory-based learning, is a machine learning approach that makes predictions or classifications based on the similarity between new instances and the training examples Read more.
Instance-based learning, also known as lazy learning or memory-based learning, is a machine learning approach that makes predictions or classifications based on the similarity between new instances and the training examples Read more.
The Internet of Things (IoT) refers to the network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity that enables them to connect and exchange data over the internet Read more.
An iterative method, in the context of mathematics or computer science, refers to a procedure or algorithm that repeatedly performs a sequence of steps or calculations to approximate a solution Read more.
JavaScript Object Notation (JSON) is a lightweight data interchange format that is widely used for storing and exchanging structured data between systems Read more.
Joint analysis refers to the process of examining and analyzing multiple variables or factors together to gain a comprehensive understanding of their relationships, interactions, and combined effects Read more.
A joint distribution, in probability theory and statistics, refers to the probability distribution of multiple random variables considered together Read more.
A joint probability distribution, also known as a bivariate probability distribution, is a probability distribution that describes the probabilities of different combinations of values for two or more random variables occurring simultaneously Read more.
K-fold cross-validation is a technique used in machine learning and statistics to assess the performance and generalization ability of a model Read more.
K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into K distinct clusters based on their similarity Read more.
K-medoids clustering is a variation of the K-means clustering algorithm that uses representative data points, called medoids, instead of centroids to define cluster centers Read more.
K-nearest neighbors (KNN) is a supervised machine learning algorithm used for classification and regression tasks Read more.
Kernel methods are a class of machine learning algorithms that enable nonlinear learning by implicitly mapping data into a high-dimensional feature space Read more.
Key Performance Indicators (KPIs) are measurable values used to assess the performance and progress of an organization, team, or individual toward achieving specific objectives or goals Read more.
A knowledge graph is a structured representation of knowledge that captures relationships between entities and concepts in a specific domain Read more.
KPI tracking refers to the process of monitoring and measuring Key Performance Indicators (KPIs) on an ongoing basis to assess progress, identify trends, and make data-driven decisions Read more.
Kullback-Leibler Divergence (KL Divergence), also known as relative entropy, is a measure of the difference between two probability distributions Read more.
L1 regularization, also known as Lasso regularization, is a technique used in machine learning and statistical modeling to add a penalty term to the objective function during training Read more.
L2 regularization, also known as Ridge regularization, is a technique used in machine learning and statistical modeling to add a penalty term to the objective function during training Read more.
Large language models are powerful artificial intelligence models that are trained on vast amounts of text data to understand and generate human-like language Read more.
Latent Dirichlet Allocation (LDA) is a generative statistical model used for topic modeling, a technique for uncovering hidden themes or topics in a collection of documents Read more.
Latent Semantic Analysis (LSA), also known as Latent Semantic Indexing (LSI), is a mathematical technique used for analyzing relationships between a set of documents and the terms they contain Read more.
The latent space refers to a lower-dimensional representation or subspace where data points are mapped or encoded Read more.
In machine learning, the learning rate is a hyperparameter that determines the step size at which an optimization algorithm updates the model parameters during the training process Read more.
Linear Discriminant Analysis (LDA) is a dimensionality reduction technique commonly used in machine learning and pattern recognition Read more.
Linear regression is a supervised machine learning algorithm used to model the relationship between a dependent variable and one or more independent variables Read more.
Logistic regression is a popular statistical model used for binary classification problems. Despite its name, logistic regression is a classification algorithm rather than a regression algorithm Read more.
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is designed to effectively capture and model long-term dependencies in sequential data Read more.
A loss function, also known as a cost function or objective function, is a mathematical function that quantifies the discrepancy between the predicted values of a model and the true values of the target variable Read more.
Low-code AI development refers to the practice of creating artificial intelligence (AI) applications using platforms or tools that require minimal manual coding Read more.
Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed Read more.
Machine learning pipelines refer to a series of interconnected steps or processes that are sequentially executed to build and deploy machine learning models Read more.
Market basket analysis, also known as association analysis or affinity analysis, is a data mining technique that identifies relationships and patterns among items frequently purchased or used together by customers Read more.
A Markov chain is a mathematical model that represents a sequence of events or states where the probability of transitioning from one state to another depends only on the current state Read more.
A Markov Decision Process (MDP) is a mathematical framework used to model decision-making problems in a stochastic environment Read more.
Maximum a Posteriori (MAP) is a statistical estimation method used to find the most probable value or set of values of unknown parameters given observed data and prior knowledge Read more.
Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a statistical model based on observed data Read more.
Mean Absolute Error (MAE) is a metric commonly used to evaluate the performance of a regression model Read more.
Mean Squared Error (MSE) is a commonly used metric for evaluating the performance of regression models Read more.
Model deployment refers to the process of making a trained machine-learning model available for use in a production environment Read more.
Model selection is the process of choosing the best machine learning model from a set of candidate models for a given task Read more.
Multiclass classification refers to a classification task where the goal is to assign an input instance to one of several predefined classes or categories Read more.
Multilabel classification is a classification task where an input instance can be associated with multiple class labels simultaneously Read more.
A multilayer perceptron (MLP) is a type of artificial neural network that consists of multiple layers of interconnected nodes, called neurons. Read more.
The multinomial distribution is a probability distribution that generalizes the concept of the binomial distribution to situations where there are more than two possible outcomes or categories. Read more.
Multivariate analysis is a statistical approach that deals with the analysis of data that involves multiple variables simultaneously. Read more.
Natural Language Generation (NLG) is a subfield of artificial intelligence (AI) that focuses on generating human-like text or speech from structured data or other non-linguistic inputs Read more.
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language Read more.
Natural Language Understanding (NLU) is a subfield of natural language processing (NLP) that focuses on the comprehension and interpretation of human language by computers Read more.
Nearest Centroid Classification, also known as Centroid-based Classification, is a simple and intuitive classification algorithm that assigns new data points to the class whose centroid (mean) is closest to the data point Read more.
Negative log-likelihood (NLL), also known as cross-entropy loss, is a commonly used loss function in machine learning, particularly in classification tasks Read more.
Network analysis, also known as network science or graph analysis, is a field of study that focuses on the analysis and understanding of complex systems represented as networks or graphs Read more.
Neural Architecture Search (NAS) is a method in the field of deep learning that automates the process of designing neural network architectures Read more.
Neural networks, also known as artificial neural networks (ANNs) or simply neural nets, are a fundamental concept in the field of deep learning and machine learning Read more.
The No Free Lunch (NFL) theorem is a fundamental concept in machine learning and optimization theory Read more.
Nonlinear regression is a statistical modeling technique used to estimate the parameters of a nonlinear relationship between a dependent variable and one or more independent variables Read more.
Nonlinear regression is a statistical modeling technique used to estimate the parameters of a nonlinear relationship between a dependent variable and one or more independent variables Read more.
Normalization, also known as data normalization or feature scaling, is a preprocessing technique used in machine learning to standardize the range or distribution of numerical features or variables Read more.
In statistics, the null hypothesis (denoted as H0) is a statement that assumes there is no significant difference or relationship between variables or that any observed difference or relationship is due to chance Read more.
Object detection is a computer vision task that involves identifying and localizing objects within an image or a video Read more.
One-class classification, also known as one-class learning or anomaly detection, is a machine learning task where the goal is to classify instances into one specific class, often referred to as the target class or the positive class Read more.
One-hot encoding is a technique used in data preprocessing to represent categorical variables as binary vectors Read more.
Online machine learning, also known as incremental or streaming machine learning, is an approach to machine learning where models are continuously updated as new data arrives in a sequential manner Read more.
Optical Character Recognition (OCR) is a technology that converts scanned images or printed text into machine-readable text Read more.
The out-of-bag (OOB) error is a technique used in ensemble learning, specifically in the random forest algorithm, to estimate the performance of the model without the need for a separate validation set Read more.
Outlier detection, also known as anomaly detection, is the process of identifying observations or data points that deviate significantly from the expected or normal behavior in a dataset Read more.
Overfitting is a common problem in machine learning where a model learns the training data too well, to the extent that it performs poorly on unseen or new data Read more.
Parametric models are a class of statistical or machine learning models that make explicit assumptions about the functional form or distribution of the underlying data Read more.
Pattern recognition is the process of identifying patterns or regularities in data or observations and making inferences or predictions based on those patterns Read more.
PCA (Principal Component Analysis) is a statistical technique used for dimensionality reduction and data exploration Read more.
The Pearson correlation coefficient, also known as Pearson’s r or simply correlation coefficient, is a statistical measure that quantifies the linear relationship between two variables Read more.
The perceptron is a type of artificial neuron or a basic building block of artificial neural networks. It was introduced by Frank Rosenblatt in 1957 as a binary classification algorithm Read more.
Perimeter technology, also known as perimeter security or perimeter protection, refers to a set of security measures and technologies designed to secure the perimeter or boundary of a physical or digital space Read more.
The Poisson distribution is a discrete probability distribution that describes the number of events that occur within a fixed interval of time or space, given a known average rate of occurrence Read more.
Polynomial regression is a type of regression analysis that models the relationship between the independent variable(s) and the dependent variable using a polynomial function Read more.
In the context of convolutional neural networks (CNNs), a pooling layer is a type of layer that performs a downsampling operation on the input data Read more.
In the context of convolutional neural networks (CNNs), a pooling layer is a type of layer that performs a downsampling operation on the input data Read more.
The precision-recall curve is a graphical representation of the performance of a binary classification model, particularly in scenarios where the class distribution is imbalanced Read more.
Predictive analytics is the practice of extracting insights and making predictions about future outcomes or events based on historical data and statistical modeling techniques Read more.
Prescriptive analytics is an advanced form of analytics that goes beyond descriptive and predictive analytics Read more.
Principal Component Analysis (PCA) is a dimensionality reduction technique used to identify patterns and structures in high-dimensional data Read more.
Q-learning is a popular reinforcement learning algorithm used for solving Markov decision processes (MDPs) Read more.
Qualitative analysis is a research method used to interpret and understand non-numerical data or information Read more.
Quantization is a process in which continuous or analog data is converted into a discrete representation Read more.
Quantum computing is a rapidly evolving field of study and technology that explores the principles of quantum mechanics to develop new computational models and algorithms Read more.
Quantum machine learning is an emerging field that combines principles from quantum computing and machine learning Read more.
The Radial Basis Function (RBF) is a mathematical function commonly used in machine learning and data analysis Read more.
Random Forest is a popular ensemble learning algorithm used for both classification and regression tasks in machine learning Read more.
Random initialization refers to the process of assigning initial values to the parameters of a machine learning model randomly Read more.
A recommendation engine, also known as a recommender system, is a type of information filtering system that suggests items to users based on their preferences, past behavior, or other relevant data Read more.
A Recurrent Neural Network (RNN) is a type of neural network designed for processing sequential data, where the output of the network at a given time step is influenced by the previous inputs and the internal state of the network Read more.
A Recurrent Neural Network (RNN) is a type of neural network designed for processing sequential data, where the output of the network at a given time step is influenced by the previous inputs and the internal state of the network Read more.
Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model becomes too complex and starts to fit the training data too closely, resulting in poor generalization to new, unseen data Read more.
In reinforcement learning, the reinforcement signal, also known as the reward signal, is a crucial component of the learning process
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Residual analysis is a technique used in statistics and regression analysis to assess the quality of a regression model by examining the residuals, which are the differences between the observed values and the predicted values from the model Read more.
Residual Network (ResNet) is a deep learning architecture that was introduced by Kaiming He et al Read more.
A Restricted Boltzmann Machine (RBM) is a type of generative stochastic artificial neural network Read more.
Ridge regression is a regularization technique used in linear regression models to mitigate the problem of multicollinearity and overfitting Read more.
Robotic Process Automation (RPA) refers to the use of software robots or “bots" to automate repetitive, rule-based tasks and processes in organizations Read more.
Root Mean Square Error (RMSE) is a commonly used metric for evaluating the performance of a regression model Read more.
Root Mean Squared Logarithmic Error (RMSLE) is an evaluation metric commonly used in regression problems, particularly when the target variable has a wide range of values Read more.
Rule induction is a machine-learning technique that involves the discovery of patterns or rules in data Read more.
Rule-based systems, also known as rule-based reasoning systems or expert systems, are computational models that use a collection of if-then rules to make decisions or perform tasks Read more.
Sampling methods are techniques used in statistics and data analysis to select a subset of individuals or observations from a larger population Read more.
Self-Organizing Maps (SOM), also known as Kohonen maps, are unsupervised machine learning models used for clustering and visualization of high-dimensional data Read more.
Semi-supervised learning is a machine learning paradigm that falls between supervised learning and unsupervised learning Read more.
Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or subjective opinion expressed in a piece of text
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Sequence-to-Sequence (Seq2Seq) is a deep learning architecture used for tasks that involve transforming one sequence of data into another Read more.
Shallow learning, also known as shallow machine learning or traditional machine learning, refers to a class of machine learning algorithms that typically involve a single layer of data transformation and learning Read more.
The sigmoid activation function, also known as the logistic function, is a widely used activation function in neural networks Read more.
The sigmoid function, also known as the logistic function, is a mathematical function that maps real-valued numbers to a range between 0 and 1 Read more.
Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three separate matrices Read more.
Singular Value Thresholding (SVT) is a technique used for matrix denoising and low-rank matrix recovery Read more.
Singular Value Thresholding (SVT) is a technique used for matrix denoising and low-rank matrix recovery Read more.
Spatial convolution, also known as convolutional operation or spatial filtering, is a fundamental operation in convolutional neural networks (CNNs) used for processing and analyzing images or spatial data Read more.
Speech recognition, also known as automatic speech recognition (ASR) or speech-to-text conversion, is the technology that converts spoken language into written text Read more.
Stacking ensemble, also known as stacked generalization, is a machine learning technique that combines multiple models, called base models or learners, to make predictions Read more.
Statistical analysis refers to the collection, interpretation, and presentation of data using statistical methods Read more.
Statistical inference is the process of drawing conclusions or making predictions about a population based on sample data Read more.
Statistical significance refers to the likelihood that an observed result or difference in data is not due to random chance but represents a meaningful relationship or effect Read more.
Stratified sampling is a sampling technique used in statistics and research to ensure that a representative sample is obtained from a population by dividing it into homogeneous subgroups, known as strata, and then sampling from each stratum in proportion to its size or importance Read more.
Supervised learning is a machine learning technique in which an algorithm learns a mapping between input data and corresponding output labels or target variables Read more.
A Support Vector Machine (SVM) is a powerful and versatile supervised learning algorithm used for classification and regression tasks Read more.
Support Vector Regression (SVR) is a supervised learning algorithm that is used for regression tasks Read more.
Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data Read more.
The hyperbolic tangent function, also known as the tanh function, is a mathematical function commonly used in machine learning and neural networks Read more.
Target encoding, also known as mean encoding or likelihood encoding, is a technique used in machine learning and predictive modeling to encode categorical variables with the target variable’s mean or probability Read more.
The target variable, also known as the dependent variable or response variable, is the variable in a machine learning or statistical model that the model aims to predict or explain based on the input variables or features Read more.
Temporal Difference (TD) learning is a reinforcement learning technique that combines aspects of dynamic programming and Monte Carlo methods to learn from experience in sequential decision-making tasks Read more.
Text classification, also known as text categorization, is a natural language processing (NLP) task that involves automatically assigning predefined categories or labels to textual data based on its content Read more.
Text generation, also known as language generation, is the process of generating coherent and meaningful text based on a given prompt or set of input conditions Read more.
Text mining, also known as text analytics, is the process of extracting meaningful information and insights from unstructured text data Read more.
Text preprocessing is an essential step in natural language processing (NLP) that involves cleaning and transforming raw text data into a format suitable for further analysis and modeling Read more.
Time series analysis is a statistical method for analyzing and forecasting data that is collected over time at regular intervals Read more.
Time series clustering is a technique used to group similar time series data into clusters based on their patterns, trends, or other characteristics Read more.
Time series forecasting is a process of predicting future values or trends based on historical time series data Read more.
Top-Down Induction of Decision Trees (TDIDT) is a popular algorithm for constructing decision trees from labeled training data Read more.
In machine learning, the training set is a subset of labeled data used to train a machine learning model Read more.
Transfer learning is a machine learning technique that leverages knowledge learned from one task or domain and applies it to a different but related task or domain Read more.
Tree pruning is a technique used to reduce the size of a decision tree by removing unnecessary branches or nodes Read more.
Uncertainty estimation is the process of quantifying the uncertainty associated with predictions made by a machine learning model Read more.
Undersampling is a technique used in imbalanced machine learning datasets to address the problem of class imbalance Read more.
Unstructured data analysis refers to the process of extracting valuable insights, patterns, and meaning from unstructured data sources Read more.
Unsupervised feature learning is a machine learning technique that aims to automatically discover meaningful representations or features from unlabeled data without the need for explicit labels or supervision Read more.
Unsupervised learning is a branch of machine learning where the goal is to extract patterns, relationships, and structures from unlabeled data Read more.
User segmentation, also known as customer segmentation, is the process of dividing a target audience or customer base into distinct groups based on shared characteristics, behaviors, preferences, or other relevant criteria Read more.
A validation curve, also known as a model complexity curve, is a graphical tool used to evaluate the performance of a machine learning model across a range of different hyperparameter values Read more.
In machine learning, a validation set, also known as a holdout set, is a portion of the labeled dataset that is used to evaluate the performance of a trained model Read more.
A Variational Autoencoder (VAE) is a type of generative model that combines elements of traditional autoencoders and variational inference Read more.
Variational Bayes (VB) is a technique used in Bayesian inference to approximate the posterior distribution of model parameters when exact inference is intractable Read more.
Variational inference is a technique used in Bayesian statistics and machine learning to approximate complex posterior distributions when exact inference is intractable Read more.
Vector Autoregression (VAR) is a time series model used to analyze the dynamic relationship between multiple variables Read more.
Vectorization is a technique in computer programming and data processing that allows operations to be performed on entire arrays or matrices of data rather than individual elements Read more.
Video classification refers to the task of automatically categorizing or labeling videos into predefined classes or categories based on their content Read more.
A virtual assistant, also known as an AI assistant or chatbot, is a software program designed to provide users with assistance and perform various tasks using natural language processing and artificial intelligence techniques Read more.
Visual analytics is an interdisciplinary field that combines interactive visualizations, data analysis techniques, and human cognition to support data exploration, analysis, and decision-making Read more.
Voice recognition, also known as speech recognition or automatic speech recognition (ASR), is a technology that converts spoken language into written text or commands Read more.
Wasserstein distance, also known as Earth Mover’s distance (EMD) or Wasserstein metric, is a measure of dissimilarity between two probability distributions Read more.
Wavelet transform is a mathematical tool used for analyzing signals and data in both time and frequency domains Read more.
Web analytics refers to the collection, analysis, and interpretation of data related to website usage and user behavior Read more.
Web scraping refers to the automated process of extracting data from websites Read more.
Web search refers to the process of using a search engine, such as Google, Bing, or Yahoo, to find information on the World Wide Web Read more.
Weight decay, also known as L2 regularization or ridge regularization, is a technique used in machine learning to prevent overfitting and improve the generalization performance of a model Read more.
Weight initialization is a crucial step in training neural networks. It refers to the process of setting initial values for the weights of the network’s neurons Read more.
Word embedding is a technique used in natural language processing (NLP) and machine learning to represent words as dense, continuous vectors in a high-dimensional space Read more.
Workflow automation refers to the process of automating repetitive tasks, activities, or processes within an organization to streamline operations, improve efficiency, and reduce manual effort.
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X-ray image analysis refers to the process of analyzing and interpreting images obtained from X-ray imaging techniques, such as radiography, computed tomography (CT), and mammography Read more.
XAI, or explainable AI, refers to the field of research and techniques focused on making artificial intelligence (AI) models and their decisions more transparent, interpretable, and understandable to humans Read more.
XML (eXtensible Markup Language) is a markup language that is widely used for representing and structuring data in a hierarchical format Read more.
YANN, or Yet Another Neural Network, is a deep learning framework built in PyTorch. It is designed to be easy to use and to provide a high level of flexibility for developers Read more.
Yield analysis is a process used in manufacturing and production to evaluate the quality and efficiency of a production system or process Read more.
Zero-code analytics refers to the use of analytics tools and platforms that require little to no coding or programming skills to perform data analysis and generate insights Read more.
Zero-Inflated Poisson (ZIP) is a statistical model used to analyze count data that exhibits excessive zero values Read more.
Zero-shot learning is a machine learning technique that allows a model to recognize and classify objects or concepts that it has never seen before Read more.