1. Image Classification Datasets: These datasets contain images with corresponding labels, used for training and evaluating machine learning models for tasks such as object recognition, scene classification, and facial recognition. 2. Natural Language Processing (NLP) Datasets: These datasets contain text data, such as articles, reviews, and social media posts, along with corresponding annotations or labels. They are used for training and testing NLP models for tasks such as sentiment analysis, text classification, and machine translation. 3. Time Series Datasets: These datasets contain sequential data points collected over time, such as stock prices, weather measurements, or sensor readings. They are used for training and evaluating machine learning models for tasks such as forecasting, anomaly detection, and trend analysis. 4. Recommender System Datasets: These datasets contain user-item interactions, such as ratings, clicks, or purchases, and are used for training recommendation algorithms. They are commonly used in e-commerce, social media, and streaming services to provide personalized recommendations to users. 5. Healthcare Datasets: These datasets contain medical records, patient information, and clinical data, and are used for various healthcare analytics tasks such as disease diagnosis, patient monitoring, and treatment planning. They may also include genetic data, imaging studies, and electronic health records. 6. Social Network Datasets: These datasets contain information about relationships between individuals or entities, such as friend connections, followers, and interactions. They are used for modeling social networks, analyzing influence networks, and detecting communities within networks.