Online Learning

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

Supervised Learning

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Unsupervised Learning

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

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Multi-Task Learning

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

Batch Learning

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

Active Learning

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

Ensemble Learning

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

Feature Selection

The process of identifying and selecting the most relevant input variables for a machine learning model.

Epoch

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

Model Serialisation

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

Model Serving

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

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

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

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

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

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

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

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