DBSCAN

Dimensionality Reduction

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

Principal Component Analysis

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

t-SNE

t-Distributed Stochastic Neighbour Embedding — a technique for visualising high-dimensional data in two or three dimensions.

UMAP

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

Clustering

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

K-Means Clustering

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

Hierarchical Clustering

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

Association Rule Learning

A method for discovering interesting relationships and patterns between variables in large datasets.

Collaborative Filtering

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

Content-Based Filtering

A recommendation approach that suggests items similar to those a user has previously liked, based on item attributes.

Matrix Factorisation

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

A versioned catalogue of trained machine learning models with metadata, lineage, and approval workflows, enabling reproducible deployment and governance at enterprise scale.

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.

Synthetic Minority Over-sampling Technique — a method for addressing class imbalance by generating synthetic examples of the minority class.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

A machine learning paradigm where models learn from a continuous stream of data, accumulating knowledge over time without forgetting previously learned information.

A form of regression analysis where the relationship between variables is modelled as an nth degree polynomial.

The end-to-end automation of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and deployment, making ML accessible to non-experts.

An online learning algorithm that balances exploration of new options with exploitation of known good options to maximise reward.