Notes by Lex Toumbourou

Data Augmentation in Machine Learning is the process of expanding a dataset by making artificial modifications to existing data. In image classification, this can include techniques like rotation, stretching, and skewing, while in audio, it might involve pitch shifting or time stretching. These methods can help increase the diversity of training data, which can help with model robustness and overfitting.