Example Concept: Overfitting occurs when a model learns the training data too well, capturing noise and details that do not generalize to unseen data. To prevent this, techniques such as regularization (L1/L2), dropout, early stopping, data augmentation, and using a validation set are employed. Regularization adds a penalty to the loss function to discourage complex models, dropout randomly deactivates neurons during training to prevent co-adaptation, and early stopping halts training when performance on a validation set starts to degrade. Data augmentation artificially expands the training dataset by applying transformations, and a validation set helps monitor the model's performance on unseen data during training.