Feature learning is a process in machine learning where algorithms automatically identify and extract important characteristics or features from raw data. This helps in simplifying complex data sets, making it easier for models to understand and make predictions. For example, in image recognition, feature learning can help identify edges, shapes, and textures without manual intervention. By using techniques like neural networks or autoencoders, feature learning enables systems to improve their performance over time. This approach reduces the need for extensive feature engineering, allowing researchers and developers to focus on higher-level tasks. Ultimately, it enhances the efficiency and accuracy of various applications, such as natural language processing and computer vision.