data labeling

Data labeling involves adding informative tags, labels, or annotations to raw data like images, text, audio, and video. This process transforms unstructured data into a structured format that supervised machine learning algorithms can learn from. For example, in image recognition, data labeling might involve drawing bounding boxes around objects (like cars or pedestrians) within an image and assigning them appropriate labels. In natural language processing, it could mean tagging parts of speech or identifying named entities in text. The quality of the labeled data directly impacts the performance of the machine learning model; therefore, accurate and consistent labeling is crucial. Different techniques like bounding boxes, polygon annotation, semantic segmentation, keypoint detection, sentiment analysis, and transcription are used depending on the type of data and the specific task.