Data augmentation transforms existing training data to increase diversity without collecting new data. Why it works: More training examples, regularization effect, robustness to variations, addresses data scarcity. NLP techniques: Paraphrasing: Rephrase with LLM or back-translation. Synonym replacement: Swap words with synonyms. Random insertion/deletion/swap: Perturb text randomly. EDA (Easy Data Augmentation): Combination of simple operations. Back-translation: Translate to another language and back. Mixup: Blend examples in embedding space. Advanced techniques: Adversarial examples, counterfactual augmentation, LLM-generated variations. Vision techniques: Rotation, cropping, color jitter, cutout, mixup, cutmix, AutoAugment. Best practices: Preserve labels (augmentation shouldn't change meaning), domain-appropriate transforms, validate on non-augmented test set. Trade-offs: Too aggressive augmentation creates noise, computational overhead, may not improve if data already sufficient. Tools: TextAttack, nlpaug, Albumentations (vision). Foundational technique for improving model robustness and generalization.