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In machine learning, a feature is an individual measurable property or characteristic of the data being used to make predictions. Features are the input variables that a model uses to learn patterns and produce outputs.
Choosing the right features (feature engineering) is often more important than choosing the right algorithm — it directly determines what a model can learn.
When predicting house prices, features might include square footage, number of bedrooms, postcode, and proximity to public transport.
Dataset
A dataset is a structured collection of data used to train, validate, or test a machine learning model. It can consist of text, images, numbers, audio, or any other type of information, typically organised into rows and columns or files and labels.
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.
Dimensionality Reduction
Dimensionality reduction is a technique that simplifies complex data by reducing the number of variables (dimensions) while preserving as much meaningful information as possible. It helps make high-dimensional data easier to visualise, analyse, and process.
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.