Data Annotation
Data annotation is the process of labeling or tagging raw data (such as text, images, audio, or video) with meaningful information to create training datasets for machine learning and artificial intelligence models. It involves adding metadata, classifications, or annotations that help algorithms learn patterns and make accurate predictions. This foundational step is critical for supervised learning tasks across various domains like computer vision, natural language processing, and speech recognition.
Developers should learn data annotation when building or training machine learning models that require labeled datasets, such as for object detection, sentiment analysis, or autonomous systems. It is essential for ensuring model accuracy, reducing bias, and improving performance in real-world applications, particularly in industries like healthcare, finance, and autonomous vehicles where precise data labeling directly impacts outcomes.