K-Means Clustering

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.

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.

A centralised repository for storing, managing, and serving machine learning features, ensuring consistency between training and inference environments across an organisation.

Combining multiple machine learning models to produce better predictive performance than any single model.

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

A machine learning approach where models discover patterns and structures in data without labelled examples.

A classification algorithm that models the probability of a binary outcome using a logistic function.

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

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