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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.
The quality and size of a dataset directly determine how well an AI model performs — the saying 'garbage in, garbage out' is especially true in machine learning.
ImageNet is a dataset containing over 14 million labelled images across 20,000 categories, widely used to train and benchmark computer vision models.
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.
Feature
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.
Supervised Learning
Supervised learning is a type of machine learning where the model is trained on labelled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict the correct output for new, unseen inputs.
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