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In AI, a model is the mathematical representation that a machine learning system builds from training data. It captures the patterns, relationships, and rules discovered during training and uses them to make predictions or generate outputs on new data.
A model is the core deliverable of any AI project — choosing, training, and evaluating models is central to building effective AI solutions.
A weather forecasting model ingests atmospheric data — temperature, humidity, pressure — and outputs predictions for tomorrow's weather conditions.
Algorithm
An algorithm is a step-by-step set of instructions that a computer follows to solve a problem or complete a task. In AI, algorithms determine how a model learns patterns from data and makes predictions.
Training Data
Training data is the collection of examples used to teach a machine learning model. The model analyses this data to discover patterns and relationships, which it then uses to make predictions or generate outputs on new, unseen data.
Inference
Inference is the process of using a trained AI model to make predictions or generate outputs on new, unseen data. While training is about learning patterns, inference is about applying what the model has learned to real-world inputs.
Our programme follows a structured Level 4 curriculum with project-based learning, practical workflows, and guided implementation across business and career use cases. Funded route available for UK citizens and ILR holders.