Representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data. These representations capture the underlying structure and patterns in the data, making it easier for machine learning models to extract relevant features and make accurate predictions or decisions. Representation learning techniques often involve training neural networks or other models to automatically learn these representations from raw data, without the need for manual feature engineering. This can lead to more efficient and effective machine learning models, particularly in tasks involving complex or high-dimensional data.