Answer / Ramesh Babu

Data augmentation in Machine Learning refers to a strategy that enables the creation of additional training samples by applying transformations (such as rotations, translations, or flips) to existing data, with the aim of increasing the size and diversity of the dataset and improving model generalization.

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