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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.
The quality, quantity, and representativeness of training data are the single biggest factors in determining how well an AI model performs in the real world.
To train a model that detects spam emails, you would need thousands of emails labelled as either 'spam' or 'not spam' for the model to learn from.
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.
Model
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.
AI Bias
AI bias occurs when a system produces results that are systematically prejudiced due to flawed assumptions in the training data or algorithm. It can reflect and amplify existing societal inequalities, leading to unfair outcomes for certain groups.
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