Data Annotation

Data annotation is the process of labeling data to make it understandable and usable for machine learning models. This involves tagging or categorizing data points, such as images, text, or audio, with relevant information that helps algorithms learn from the data. Common characteristics include the use of various annotation techniques, such as bounding boxes for images or sentiment labels for text. Data annotation is crucial in supervised learning, where models require labeled datasets to train effectively. It is widely used in fields like computer vision, natural language processing, and autonomous systems.