Data Science and Machine Learning Glossary


AI text classifier

A machine learning model designed to categorize or classify text data into predefined categories or labels.

AI-powered data insights

Insights generated by artificial intelligence algorithms from analyzing large datasets to discover patterns and trends.

AI/ML Collaboration

Collaborative efforts between teams or individuals working on artificial intelligence and machine learning projects.


A common evaluation metric used in classification tasks, representing the percentage of correctly predicted instances out of the total instances in a dataset, often used to measure the overall performance of a machine learning model.


A set of rules or procedures used by a machine learning model to make predictions or decisions based on input data.

Analyze Datasets

The process of examining and exploring datasets to gain a better understanding of their content, structure, and potential insights.

Association Rule Learning

A rule-based algorithm that identifies patterns or associations in data, often used for market basketanalysis or recommendation systems.

Auto ML (Automated Machine Learning)

The automation of the machine learning pipeline, including tasks like feature selection, modelselection, and hyper parameter tuning.

Automated data analysis

The use of automated tools and algorithms to perform data analysis tasks without manual intervention.


Bias-Variance Trade-off

A concept in machine learning thatrefers to the balance between model bias (underfitting) and variance(overfitting), and the need to find an optimal trade-off to achieve the bestmodel performance.



A type of machine learning task that involves assigning input data points to predefined categories or classes based on their features, typically used for tasks such as spam detection, imagerecognition, or sentiment analysis, where the goal is to accurately predict the class label of new data points.


The process of grouping similar data points together based on their similarity or distance metrics, commonly used for unsupervised learning tasks.

Collective Artificial Intelligence

A collaborative approach to AI, involving multiple contributors or stakeholders in the development and deployment of AI models.

Compare datasets

The act of comparing two or more data sets to identify similarities, differences, or discrepancies.


Refers to the processing power or computational resources required to train, evaluate, and deploy machine learning models. It includes the hardware (such as CPUs, GPUs, and TPUs) and software (such as frameworks, libraries, and algorithms) used for performing calculations, optimizing models, and executing inference tasks.

Confusion Matrix

A matrix used to visualize the performance of a classification model, showing the number of true positive, true negative, false positive, and false negative predictions.

Continuous Delivery (CD)

A practice in ML Ops that goes beyond continuous integration and involves automatically deploying tested and approved changes to production, ensuring a rapid and reliable release process for machine learning models and pipelines.

Continuous Integration (CI)

A practice in ML Ops that involves regularly integrating code changes into a shared repository, followed by automated build and testing to detect and fix integration issues early in the development process.


A technique used to evaluate the performance of a machine learning model by dividing the data into multiple folds, training the model on different subsets of the data, and averaging the results to get a more reliable estimate of the model's performance.



Refers to the raw information that is used to train, validate, and test machine learning models. Data can come in various formats,such as structured, unstructured, or semi-structured, and can include text,images, audio, video, sensor readings, or other types of digital or analog data.

Data Drift

A phenomenon in machine learning where the distribution or characteristics of the incoming data used for model inferencechanges over time, leading to a degradation in model performance and accuracy.

Data Science Collaboration Tool

Tools and platforms that facilitate collaboration among data science teams.

Data Science Collaboration Tool:

Tools and platforms that facilitate collaboration among data science teams.

Data Similarity

The measurement of how similar or dissimilar two or more data points are in a dataset, often used in clusteringand recommendation systems.

Data aggregation software

Tools and platforms designed to collect and combine data from various sources into a single dataset.

Data analysis companies

Organizations specializing in providing data analysis services and solutions to businesses and industries.

Data cataloging solutions

Software and tools that help organize and manage datasets, making them searchable and accessible.

Data centric

An approach that places data at the center of decision-making and operations, emphasizing the importance of high-qualitydata.

Data centric AI

AI models and systems that rely on high-quality data as their primary input for training and decision-making.

Data cleaning automation

Automation techniques and tools used to clean and preprocess data to improve its quality and consistency.

Data collaboration platform

A platform that facilitates collaboration among data professionals and teams working on data-relatedprojects.

Data collaboration software

Software designed to enable collaboration and sharing of data-related information among team members.

Data engineering platforms

Integrated platforms for managing and processing data, including data transformation and ETL (Extract,Transform, Load) tasks.

Data engineering solution

A comprehensive solution for data engineering tasks, including data integration, transformation, and storage.

Data engineering tools

Software tools used by data engineers to perform tasks related to data processing and transformation.

Data exploration tools

Tools that assist in visually exploring and analyzing datasets to discover insights and patterns.

Data feature extraction

The process of selecting and extracting relevant features or attributes from raw data for use in machinelearning models.

Data governance software

Software that helps establish and enforce data governance policies and practices within organizations.

Data integration solutions

Solutions that facilitate the integration of data from multiple sources to provide a unified view.

Data labeling solutions:

Tools and platforms for labeling and annotating data, often used for training machine learning models.

Data monitoring tools

Tools used to continuously monitor data quality, integrity, and performance in real-time.

Data pattern recognition

The identification of recurring patterns or trends in data using statistical or machine learning techniques.

Data preprocessing automation

Automation of data preparation tasks, including cleaning, transformation, and feature engineering.

Data profiling techniques

Methods for examining and assessing the content and structure of datasets.

Data profiling tools

Tools that analyze and provide insights into the structure, quality, and characteristics of datasets.

Data quality assessment

The process of evaluating and measuring the quality and reliability of data.

Data quality metrics

Quantifiable measures used to assess the quality of data, including accuracy, completeness, and consistency.

Data quality tools

Software tools designed to identify and address data quality issues within datasets.

Data transformation techniques

Methods for modifying, enriching, or reshaping data to make it suitable for analysis or modeling.

Data transformation tools

Tools used to perform data transformation tasks, such as ETL processes.

Data trend analysis

The examination of data over time to identify trends, patterns, or anomalies.

Decision Trees

A tree-based algorithm that recursively splits the data based on feature values to make decisions or predictions.

Deep Learning

A subset of machine learning that involve straining neural networks with multiple layers to learn complex patterns and representations from large amounts of data, often used for tasks such as image recognition, natural language processing, and speech recognition.


A set of practices that involve collaboration and integration between development (Dev) and operations (Ops) teams to streamline software development, deployment, and operation processes, often applied to ML Ops to enhance collaboration between data scientists, engineers, and IT operations.

Dimensionality Reduction

Algorithms that reduce the number of features in the data while retaining important information to simplify analysis or visualization.


EDA Tools (Exploratory Data Analysis Tools)

Software and techniques used to explore and visualize data for initial insights.


Representation of data, especially text or categorical data, in a lower-dimensional space for machine learning.

Ensemble Learning

A technique in machine learning that combines the predictions of multiple models, such as decision trees or classifiers, to improve the accuracy, robustness, and generalization of the final prediction.


A complete iteration through the entire training dataset during model training, used to update the model's parameters.

Experiment Tracking

The practice of recording, organizing, and analyzing experimental results, including hyperparameter configurations, model performance metrics, and associated metadata, to enable reproducibility, comparison, and optimization of machine learning experiments.


Feature Engineering

The process of selecting, transforming ,or creating relevant features from raw data to improve the performance and interpretability of machine learning models.

Feature Selection

The process of choosing a subset of the most relevant features from the original set of features to reducedimensionality, improve model performance, and reduce computational complexity.

Feature engineering best practices

Strategies and guidelines for creating informative features for machine learning models.

Feature selection techniques:

Methods for choosing the most relevant features or variables for use in machine learning models.


Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating models or algorithms that can generate new data or content that is similar to existing data. These models can generate new images, text, music, videos, or other types of content that are not explicitly programmed, but rather learned from existing data.

Gradient Boosting Algorithms

Ensemble learning algorithms that combine weak learners sequentially to create a strong learner with improved accuracy, e.g., XG Boost, and LightGBM.

Gradient Descent

An optimization algorithm used in machine learning to iteratively update the model parameters based on the gradient of the loss function with respect to the parameters, in order to minimize the error and improve model performance.


Hyperparameter tuning methods:

Techniques for optimizing the hyperparameters of machine learning models to improve their performance.


Parameters of a machine learning model that are set before the training process, such as learning rate, batch size, or the number of epochs, and can be tuned to optimize the model's performance.



A single data point or observation in a dataset, used as input for training or testing a machine learning model.


The process of repeating the training and evaluation steps of a machine learning model multiple times in order to refine and improve its performance, typically by adjusting hyperparameters, updating weights, and fine-tuning the model based on feedback from previous iterations.


k-Nearest Neighbors (k-NN)

A lazy learning algorithm thatclassifies new data points based on the majority class of their k nearestneighbors.


LLMs (Large Language Models)

LLMs are a type of machine learning model that are specifically designed for natural-language processing tasks, such as language generation, language understanding, sentiment analysis,and machine translation. LLMs learn patterns and structures from large amounts of text data to generate text or analyze text-based inputs.


The target or output variable in a supervised learning task, representing the value to be predicted by a machine learning model.

Linear Regression

A supervised learning algorithm that models the relationship between a dependent variable and one or more independent variables as a linear equation.

Logistic Regression

A binary classification algorithm that models the probability of an input belonging to a certain class using alogistic function.


A mathematical function that measures the discrepancy between the predicted output and the actual output (ground truth) of a machine learningmodel during training, used as a guide to adjust the model's parameters to minimize the error and improve its performance.


ML Engineering

The discipline of applying engineering principles to develop, deploy, and maintain machine learning systems.

ML Model

The mathematical and algorithmic representations used in machine learning for tasks like classification and regression.

ML Ops (Machine Learning Operations)

The set of practices and tools for managing and deploying machine learning models.

ML algorithm selection

The process of choosing the most suitable machine learning algorithm for a given problem.

ML deployment automation

Automating the process of deploying machine learning models into production environments.

ML deployment strategies

Strategies and approaches for deploying machine learning models in real-world applications.

ML feature engineering

The process of creating new features from existing data to improve the performance of machine learning models.

ML interpretability techniques

Methods for understanding and explaining the decisions made by machine learning models.

ML model accuracy improvement

Techniques and strategies for enhancing the accuracy and performance of machine learning models.

ML model comparison

The evaluation and comparison of multiple machine learning models to determine the best-performing one.

ML model deployment

The process of deploying trained machine learning models for use in production systems.

ML model evaluation

The assessment of the performance and effectiveness of machine learning models using various metrics.

ML model explainability

The ability to interpret and explain the decisions and predictions made by machine learning models.

ML model fine-tuning

The process of adjusting hyperparameters and model configurations to optimize performance.

ML model governance

The establishment of policies and processes to ensure the responsible and ethical use of machine learning models.

ML model management

The ongoing maintenance, monitoring ,and updating of machine learning models in production.

ML model optimization

Techniques for improving the efficiency and resource utilization of machine learning models.

ML model performance evaluation

The assessment of a machine learning model's performance using metrics and testing.

ML model scalability

The ability of a machine learning model to handle increasing amounts of data or user interactions.

ML model validation

The process of testing and verifying the accuracy and reliability of machine learning models.

ML model versioning

The practice of keeping track of different versions of machine learning models to ensure reproducibility.

ML workflow automation

Automating the steps involved in designing, training, and deploying machine learning models.

Machine Learning Collaboration Tool

Tools and platformsthat facilitate collaboration among teams working on machine learning projects.

Machine Learning Collaboration Tool

Tools and platformsthat facilitate collaboration among teams working on machine learning projects.

Machine learning tools

Software and libraries used to develop, train, and deploy machine learning models.

Model Deployment

The process of making a trained machine learning model available for prediction or inference in a productionenvironment, typically involving deploying the model to a server or cloud-based infrastructure for serving predictions

Model Deployments

The process of making machine learning models available for use in real-world applications.

Model Evaluation:

The process of assessing the performance of a trained machine learning model using various metrics, such as accuracy, precision, recall, F1 score, etc., to measure its effectiveness in making predictions or classifications.

Model Experiments

Systematic tests and iterations performed on machine learning models to improve their performance.

Model Governance

The practice of establishing policies, guidelines, and controls for managing machine learning models throughout their lifecycle, including model development, deployment, monitoring, and retirement, to ensure compliance, security, and reliability.

Model Monitoring

The process of tracking and measuring the performance, behavior, and health of deployed machine learning models in production, to detect and resolve any issues or deviations from expected behavior.

Model Registry

A centralized repository or catalog that stores metadata, configuration, and artifacts of machine learning models, such as trained models, hyperparameters, and associated documentation, to enableeasy discovery, sharing, and versioning of models.

Model Retraining

The process of periodically updating and retraining machine learning models with new data to ensure that the modelremains accurate and relevant over time, accounting for changes in data distribution or business requirements.

Model Serving

The process of making machine learning modelsavailable for prediction or inference by receiving input data, processing itthrough the model, and returning the model's predictions or outputs to therequesting system or application.

Model Sharing

The practice of sharing trained machine learning models with other team members or the community.

Model Tuning

The process of optimizing hyperparameters or model architecture to improve the performance and accuracy of a machine learning model, often involving techniques such as grid search, random search, or Bayesian optimization.

Model Versioning

The practice of keeping track of different versions or iterations of machine learning models, including their trained parameters, hyperparameters, and associated code, to enable reproducibility, comparison, and rollback of model versions.

Model interpretability tools

Tools that provide insights into how machine learning models make decisions.


Mathematical representations or algorithms that aretrained on data to make predictions, classifications, or generate insights.


NLP (Natural Language Processing)

NLP is a subfield of artificial intelligence that focuses on the interaction between computers and human language. It involves the processing and analysis of text or speech data to enable machines to understand, interpret, and generate human language.

Naive Bayes

A probabilistic algorithm that uses Bayes' theorem to classify data based on the conditional probability of features given a class.

Neural Network

A type of machine learning model that is inspired by the structure and function of the human brain, consisting of interconnected nodes or neurons organized in layers, used for tasks such as image recognition, speech recognition, and natural language processing.

No code gen AI tools (No-Code AI Generation Tools): Tools that enable non-technical users to create AI models without coding.


A phenomenon in machine learning where a model learns to perform well on the training data but fails to generalize to new, unseen data due to excessive complexity or memorization of the training data.


PCA (Principal Component Analysis)

A dimensionalityreduction algorithm that transforms data into a lower-dimensional space whilepreserving its most important features.


A metric that measures the proportion of true positive predictions out of the total predicted positive instances, often usedin binary classification problems to assess the accuracy of positive predictions.

Predictive modeling tools:

Tools and techniques used to build models that make predictions based on data.

Product and engineering collaboration

Collaboration between product development and engineering teams to build and improve products.


Random Forests

An ensemble learning algorithm that combines multiple decision trees to improve prediction accuracy and reduce overfitting.


A type of machine learning task that involves predicting a continuous output value based on input features, typically used for estimating quantities such as price, temperature, or time, where the goal is to minimize the difference between predicted and actual values.

Reinforcement Learning:

A type of machine learning where an agent learns to make decisions in an environment by receiving feedback in the form of rewards or penalties.

Ridge Regression

A linear regression algorithm that includes a penalty term to prevent overfitting by regularizing the model's coefficients.


Text Analysis

The process of analyzing and extractinginsights from textual data, often using natural language processing techniques.

Time series analysis tools

Tools and techniques foranalyzing data that is collected or recorded over time.


Unsupervised Learning

A type of machine learning where the algorithm learns from unlabeled data, without known output labels, to findpatterns, relationships, or groupings within the data.



The coefficients or parameters learned by a machinelearning model during training, used to make predictions or decisions based oninput features.


Data aggregation

Data aggregation is the process of compiling data (often from multiple data sources) to provide high-level summary information that can be used for statistical analysis. An example of a simple data aggregation is finding the sum of the sales in a particular product category for each region.

Data analytics

Data analytics is the process of exploring, transforming, and analyzing data to identify meaningful insights and efficiencies that support decision-making.

Data applications

Data applications are applications built on top of databases that solve a niche data problem and, by means of a visual interface, allow for multiple queries at the same time to explore and interact with that data. Data applications do not require coding knowledge in order to procure or understand

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