Data Augmentation Techniques for Enhancing Machine Learning Datasets

@genaiexp Data augmentation is a powerful technique for enhancing the size and quality of training datasets. By applying transformations such as rotation, flipping, scaling, and cropping, you can create diverse variations of the original data, helping models become more robust and generalizable. Beyond simple transformations, synthetic data generation methods can augment datasets further, providing additional examples for the model to learn from. Data augmentation doesn't just improve model performance; it also helps in mitigating overfitting by exposing the model to a wide range of scenarios. Practical tools like Keras and Albumentations offer easy-to-use interfaces for implementing data augmentation, making it accessible for all levels of practitioners.