For instance, predicting whether a patient has a disease and assigning their health data to “disease” or “no disease” categories is a classification problem. Classification: Assigning test data into specific categories.Supervised ML models (see Figure 1) train and learn from correctly annotated data and solve problems such as: For supervised machine learning, labeled datasets are crucial because ML models need to understand input patterns to process them and produce accurate results. What is data annotation?ĭata annotation is the process of labeling data in various formats such as video, images, or text so that machines can understand it. This article highlights the what data annotation is, why it matters, what its techniques/types, and challenges. According to McKinsey, data annotation is one of the top limitations of AI implementation for organizations. At the same time, it is one of the most time-consuming and labor-intensive parts of ML projects. What is Synthetic Data? What are its Use Cases & Benefits?Īnnotated data is an integral part of many machine learning and artificial intelligence applications.Synthetic Data Generation: Techniques, Best Practices & Tools.5 Use Cases and Applications of Medical Sentiment Analysis.
#ANNOTATIONS MEANING HOW TO#
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