Principal Component Analysis

Dimensionality Reduction

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

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.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

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.

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

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

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.

One complete pass through the entire training dataset during the machine learning model training process.

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

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

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

A technique where knowledge gained from training on one task is applied to a different but related task.