5 Approaches to Data Labeling for Machine Learning Projects

The quality of a machine learning project comes down to how you handle three important factors: data collection, data preprocessing, and data labeling. Data labeling is integral because it's literally labeling the data that will teach your model to learn its task. However, data labeling is often time consuming and complex. For example, image recognition systems often require bounding boxes drawn around specific objects, while product recommendation and sentiment analysis systems can require complex cultural knowledge for accurate data labeling. And don't forget that a dataset could contain tens of thousands of samples in need of labeling, if not more.