• A3C (Advantage Actor-Critic): A specific algorithm of Reinforcement Learning which is used to learn the policy and value function using neural networks.
  • AdaBoost: A boosting algorithm that adaptively adjusts the weight of each data point to give more weight to the misclassified data points.
  • Algorithm: a set of instructions or rules that specify how to perform a task or solve a problem.
  • Anomaly Detection: the process of identifying data points or observations that deviate significantly from the norm or typical behavior.
  • Anomaly Detection: the process of identifying unusual or abnormal data points in a dataset.
  • ARIMA (Auto-Regressive Integrated Moving Average): A popular method of time series forecasting that combines the use of auto-regression and moving average to make predictions.
  • Artificial Intelligence (AI): the simulation of human intelligence in machines that are programmed to think and learn like humans.
  • AUC (Area Under the ROC Curve): a measure of the performance of a binary classifier, which gives the area under the ROC curve. AUC ranges in value from 0 to 1, with 1 indicating a perfect classifier and 0.5 indicating a classifier no better than random guessing.
  • AUC-ROC (Area Under the Receiver Operating Characteristic Curve): a measure of a model’s performance that compares the true positive rate and false positive rate of a binary classification model.
  • Autoencoders: A type of neural network that is trained to reconstruct its input, typically for dimensionality reduction and feature learning.
  • Bagging: A technique where multiple models are trained on random subsets of the data and the final prediction is made by averaging the predictions of all the models.
  • Batch Learning: A method of learning where the entire dataset is used to train the model.
  • Bayes’ Theorem: a mathematical formula that describes how to update the probability of a hypothesis in light of new evidence.
  • Bayesian Inference: the process of using Bayes’ theorem to infer the parameters of a model from data.
  • Bayesian Networks: a type of probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph.
  • Bayesian Optimization: an optimization method that uses Bayesian inference to model the unknown function and then selects the next point to evaluate based on the acquisition function.
  • Bayesian Statistics: a branch of statistics that uses Bayes’ theorem to update the probability of a hypothesis as more evidence or information becomes available.
  • Bias: a measure of a model’s performance that indicates the difference between the average predicted value and the true value in a regression model.
  • Bias-Variance Tradeoff: a fundamental concept in machine learning, which refers to the trade-off between the complexity of a model and its ability to fit the training data. A model with high bias is simpler and less flexible, and may underfit the data, while a model with high variance is more complex and more flexible, and may overfit the data.
  • Bias-variance trade-off: the trade-off between a model’s ability to fit the training data well (low bias) and its ability to generalize to new data (low variance).
  • Big Data: a term used to describe the large amounts of data – both structured and unstructured – that inundate a business on a day-to-day basis.
  • Bootstrapping: a technique used to estimate the performance of a model by randomly sampling the data with replacement and training the model on the samples.
  • Bullet Point List All Data Science Terminology and Related Definitions.
  • Bullet Point List All Scientific Terminology and Related Definitions.
  • CatBoost: yet another implementation of gradient boosting algorithm, which is efficient and effective for handling categorical variables.
  • Chatbots: A type of dialogue system that uses natural language processing and machine learning to simulate a conversation with a human.
  • Classification: the process of predicting the class or category of an observation based on its features.
  • Clustering: the process of grouping similar data points together based on certain features or characteristics.
  • Clustering: the process of grouping similar observations together based on certain features or characteristics.
  • Collaborative Filtering: A method used in recommender systems that generates recommendations based on the past behavior and preferences of users similar to the target user.
  • Confusion Matrix: a table that is often used to describe the performance of a classification algorithm. It allows visualization of the performance of an algorithm.
  • Confusion Matrix: a table that is used to define the performance of a classification algorithm. Where true positives, true negatives, false positives, and false negatives are summed up for each class.
  • Content-Based Filtering: A method used in recommender systems that generates recommendations based on the characteristics of the items and the preferences of the user.
  • Convolutional Neural Networks (CNNs): A type of neural network that is particularly effective for image and video data, which uses convolutional layers to automatically learn features from the data.
  • Coreference Resolution: the process of identifying when two or more expressions in a text refer to the same real-world entity.
  • Cosine Similarity: a measure of a model’s performance that is used to compare the similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.
  • Cross-validation: a technique used to assess the performance of a model by dividing the data into a training set and a test set, and then training the model on the training set and evaluating it on the test set.
  • Cross-Validation: a technique used to estimate the performance of a model on unseen data by dividing the data into training and validation sets and evaluating the model’s performance on the validation set.
  • Data Mining: the process of discovering patterns and knowledge from large amounts of data.
  • Data Preprocessing: the process of preparing data for analysis, which includes cleaning, transforming, and normalizing the data.
  • Decision Trees: a type of model that recursively splits the data into subsets based on the values of the input features, creating a tree-like structure that can be used for classification or regression tasks.
  • Deep Learning: a type of machine learning that uses neural networks with many layers (deep architectures) to learn from data.
  • Dependency Parsing: A technique used to analyze the grammatical structure of a sentence and identify the relationships between words, such as subject, object, and modifier.
  • Dialogue Systems: A system that can understand and generate natural language text to simulate a conversation with a human.
  • Dimensionality Reduction: the process of reducing the number of features in a dataset while retaining as much information as possible.
  • Dimensionality Reduction: the process of reducing the number of features or dimensions in a dataset while preserving as much relevant information as possible.
  • Discourse Analysis: the process of studying the structure and organization of written or spoken language in context.
  • DQN (Deep Q-Network): A specific algorithm of Reinforcement Learning which is used to learn the Q-values using neural networks.
  • Ensemble Learning: a method where multiple models are combined to improve the overall performance of the system.
  • Ensemble methods: A method of combining multiple models to improve the overall performance of the system. Examples of ensemble methods include bagging, boosting, and stacking.
  • Expectation-Maximization (EM): a technique used to estimate the parameters of a model when some of the data is missing or hidden.
  • Explainable AI (XAI): a branch of AI that focuses on developing models that can be understood, interpreted, and explained by humans.
  • Exponential Smoothing: A time series forecasting method that uses a weighted average of past observations to make predictions.
  • F1-Score: a measure of a model’s performance that balances precision and recall in a binary classification model.
  • Fairness: the ability of a model to make unbiased predictions and treat all groups of individuals fairly.
  • Feature Engineering: the process of creating new features or transforming existing features in order to make the data more useful for a specific model or task.
  • GAN (Generative Adversarial Networks): a type of deep learning model that consists of two neural networks, a generator and a discriminator, that are trained to work against each other in order to generate new, realistic data.
  • GANs: A type of generative model that involves training a generator model to generate new data that is similar to the training data, and a discriminator model to distinguish between real and fake data.
  • Gaussian Mixture Model: a probabilistic model that represents a mixture of multiple Gaussian distributions.
  • Generative Models: A type of model that learns to generate new data that is similar to the training data. Examples of generative models include Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).
  • Gini Coefficient: a measure of a model’s performance that is used in decision tree based models, it is based on the proportion of the total sample that would be correctly classified if the sample were divided into two groups using a single variable.
  • G-Mean: a measure of a model’s performance that is used in imbalanced classification problems, it is the geometric mean of sensitivity and specificity
  • Gradient Boosting: an ensemble technique that combines multiple weak learners to create a strong learner. It uses gradient descent algorithm to minimize the loss function.
  • Gradient Descent: an optimization algorithm used to find the values of parameters (coefficients) of a function (such as a neural network) that minimizes a cost function.
  • Grid Search: A method of hyperparameter tuning that involves specifying a set of possible values for each hyperparameter and then training a model for each combination of hyperparameter values.
  • Hybrid Recommender Systems: A method used in recommender systems that combines the approach of collaborative filtering and content-based filtering to generate recommendations.
  • Hyperparameter tuning: the process of adjusting the parameters of a model in order to improve its performance on unseen data. This can be done manually, by trying different combinations of parameters, or automatically, using techniques such as grid search or random search.
  • Hyperparameter tuning: the process of selecting the best set of hyperparameters for a model. Hyperparameters are the parameters of the model that are not learned from data.
  • Hypothesis Testing: a statistical method used to test the validity of a claim or hypothesis about a population parameter.
  • ICE (Individual Conditional Expectation): A method for visualizing the relationship between a feature and the model’s predictions for each individual instance.
  • Independent Component Analysis (ICA): A method of dimensionality reduction that separates a multivariate signal into independent non-Gaussian components.
  • Information Extraction: A technique used to automatically extract structured information from unstructured text data.
  • Information Retrieval: A technique used to search and retrieve relevant information from a large collection of text data.
  • Jaccard Similarity: a measure of a model’s performance that is used to compare the similarity between two sets of data.
  • Kappa Statistic: a measure of a model’s performance that is used to compare the agreement between two raters.
  • K-means: a popular clustering algorithm that partitions a dataset into k clusters based on their similarity.
  • Language Modeling: A technique used to predict the next word in a sequence of text data, based on the preceding words.
  • Latent Factor Models: A type of model used in collaborative filtering that attempts to learn latent factors that explain the observed user-item interactions.
  • Lift: a measure of a model’s performance that is used in marketing and customer relationship management (CRM) to determine the effectiveness of a campaign.
  • LightGBM: Another specific implementation of gradient boosting algorithm, which uses a histogram-based algorithm to reduce the amount of data that needs to be processed in each iteration and thus making the training faster.
  • LIME (Local Interpretable Model-Agnostic Explanations) : A method for explaining the predictions of any classifier by learning an interpretable model locally around the prediction.
  • Linear Regression: a statistical method used to model the relationship between a dependent variable and one or more independent variables.
  • LSTM (Long Short-term Memory): a type of Recurrent Neural Network that is able to process and understand sequences of data.
  • LSTM (Long Short-Term Memory): A type of recurrent neural network that is particularly effective for time series data as it allows for the preservation of long-term dependencies in the data.
  • Machine Learning: a type of AI that involves training a computer system to learn from data and make predictions or decisions without being explicitly programmed to do so.
  • Markov Chain Monte Carlo (MCMC): a technique used to approximate complex probability distributions by generating a Markov chain that has the desired distribution as its equilibrium distribution.
  • Matrix Factorization: A technique used in collaborative filtering to decompose a large user-item matrix into smaller, lower-dimensional matrices that can be used to generate recommendations.
  • Mean Absolute Error (MAE): a measure of a model’s performance that indicates the average absolute difference between the predicted and actual values in a regression model.
  • Model interpretability: the ability of a model to be understood and explained by humans.
  • Model Selection: the process of choosing the best model from a set of candidate models for a given dataset.
  • Named Entity Disambiguation: a process of identifying which entity a word or phrase refers to from a pre-defined set of entities.
  • Named Entity Recognition (NER): A technique used to identify and classify named entities, such as people, organizations, and locations, in text data.
  • Natural Language Processing (NLP): A branch of AI that deals with the interactions between computers and humans in natural language.
  • Neural Network: a type of machine learning model inspired by the structure and function of the human brain, which is composed of layers of interconnected nodes or artificial neurons.
  • Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, which is composed of layers of interconnected nodes or artificial neurons.
  • Neural Recommender Systems: A type of recommender system that uses neural networks to make recommendations.
  • NLP (Natural Language Processing): a branch of AI that deals with the interactions between computers and humans in natural language.
  • Online Learning: A method of learning where the model is trained incrementally on small batches of data, rather than using the entire dataset at once. This allows for the model to adapt to new data as it becomes available and can be useful for handling large and constantly changing datasets.
  • Overfitting: a problem that occurs when a model is trained too well on the training data and performs poorly on new, unseen data.
  • Overfitting: when a model is trained too well on the training data, and performs poorly on new, unseen data.
  • Part-of-Speech (POS) Tagging: A technique used to identify and classify the grammatical roles of words in text data, such as nouns, verbs, and adjectives.
  • PCA (Principal component analysis): a technique used to extract the most important features from a dataset and reduce the dimensionality of the data.
  • PDP (Partial Dependence Plot) : A method for visualizing how a feature affects a model’s predictions.
  • Policy Gradient: A specific algorithm of Reinforcement Learning which is used to learn the policy directly using gradient descent.
  • Precision and Recall : A set of metrics to evaluate the performance of a classification model, precision is the proportion of true positive to all positive predictions and recall is the proportion of true positives to all actual positives.
  • Precision-Recall Curve: a measure of a model’s performance that shows the relationship between precision and recall at different threshold settings in a binary classification model.
  • Principal Component Analysis (PCA): A popular method of dimensionality reduction that finds a new set of linearly uncorrelated variables called principal components.
  • Prophet: A popular open-source tool for time series forecasting developed by Facebook that combines elements of both traditional time series methods and machine learning.
  • Q-Learning: A specific algorithm of Reinforcement Learning which
  • Q-Learning: A specific algorithm of Reinforcement Learning which is used to learn the optimal action-selection policy using Q-values.
  • Random Forest: an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
  • Random Forest: an ensemble of decision trees, where each tree is trained on a different subset of the data and the final prediction is made by averaging the predictions of all the trees.
  • Random Search: A method of hyperparameter tuning that involves randomly selecting combinations of hyperparameter values to try.
  • Recommender systems: a system that attempts to predict what a user would like to see based on their past interactions.
  • Recommender Systems: A type of system that uses historical data to make personalized recommendations to users.
  • Recurrent Neural Networks (RNNs): A type of neural network that is particularly effective for sequential data, which uses recurrent layers to maintain a hidden state that can be used to process the data in a sequential manner.
  • Regularization: a technique used to prevent overfitting by adding a penalty term to the cost function that discourages the model from assigning too much weight to any one feature.
  • Regularization: a technique used to prevent overfitting by adding a penalty term to the model’s cost function.
  • Reinforcement Learning: a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving rewards or penalties for its actions.
  • ROC Curve: a graphical representation of the performance of a binary classifier, showing the true positive rate against the false positive rate.
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  • Root Mean Squared Error (RMSE): a measure of a model’s performance that indicates the average difference between the predicted and actual values in a regression model.
  • Root Mean Squared Error (RMSE): a measure of the difference between predicted and actual values for a dataset.
  • R-squared: a measure of a model’s performance that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s) in a linear regression model.
  • SARSA: A specific algorithm of Reinforcement Learning which is used to learn the action-value function using on-policy learning.
  • Semantic Role Labeling (SRL): the process of identifying the semantic roles of different arguments of a verb in a sentence.
  • Sentence Simplification: the process of reducing the complexity of a sentence while preserving its meaning.
  • Sentiment Analysis: A technique used to classify text data into positive, negative, or neutral categories based on the sentiment expressed in the text.
  • SHAP (SHapley Additive exPlanations) : A popular method for explaining the predictions of machine learning models.
  • Singular Value Decomposition (SVD): A method of dimensionality reduction that factorizes a matrix into its singular values and vectors.
  • Speech Recognition: A technique used to convert spoken words into text data that can be processed by a computer.
  • Speech Synthesis: A technique used to convert text data into spoken words that can be played back by a computer.
  • Speech-to-Text (STT) systems: A type of speech recognition system that converts spoken words into text data.
  • Stacking: An ensemble method that involves training a second-level model to make predictions using the output of multiple base models.
  • Supervised Learning: a type of machine learning where the model is trained on a labeled dataset, which means that the correct output or label is provided for each input.
  • Temporal Information Extraction: the process of extracting information about events and their temporal relationships from text data.
  • Text Classification: the process of classifying text data into predefined categories or topics based on its content.
  • Text Clustering: the process of grouping similar text data together based on certain features or characteristics.
  • Text Generation: A technique used to generate new text data that is similar to the training data, based on a language model.
  • Text Generation: the process of creating new text data that is similar to the training data.
  • Text mining: the process of extracting useful information from text data.
  • Text Similarity: the process of measuring the similarity between two or more pieces of text.
  • Text Summarization: A technique used to condense a large amount of text data into a shorter, more concise summary.
  • Text-to-Speech (TTS) systems: A type of speech synthesis system that converts text data into spoken words.
  • Time Series Analysis: the process of analyzing time-based data in order to understand trends, patterns, and relationships.
  • Time Series Decomposition: the process of breaking down a time series into its constituent components such as trend, seasonality, and residuals.
  • Time series forecasting: a method for predicting future values based on historical patterns in time series data.
  • Time Series Forecasting: the process of using past data to predict future values in a time series.
  • Transfer Learning: a technique where a model trained on one task is used as a starting point for a model on a second, related task.
  • t-SNE (t-Distributed Stochastic Neighbor Embedding): A method of dimensionality reduction that is particularly useful for visualizing high-dimensional data in a low-dimensional space.
  • Underfitting: when a model is not trained well enough on the training data, and performs poorly on both the training and new, unseen data.
  • Unsupervised Learning: a type of machine learning where the model is not provided with labeled data and must find patterns or relationships in the input data on its own.
  • VAEs: A type of generative model that involves training a model to learn a compact, low-dimensional representation of the data and then using this representation to generate new data.
  • Validation: the process of evaluating a model’s performance on a dataset that was not used for training.
  • Variance: a measure of a model’s performance that indicates the variability of the predictions made by a model with different training data in a regression model.
  • Variance: a measure of how much the values in a dataset deviate from the mean.
  • Word Embedding: A technique used to represent words as high-dimensional vectors that capture their meaning and context in a way that can be used by a machine learning model.
  • XGBoost : A specific implementation of gradient boosting algorithm, which is particularly efficient and effective on large datasets.

Scientific terminology and the above-mentioned related definitions encompass many fields, including biology, physics, chemistry, and more.