Data plays a crucial role in AI-based applications by providing the foundation for training machine learning models. The quality and quantity of data determine the performance and accuracy of AI models. Data is used to train, test, and validate models so they can make predictions, classifications, or decisions based on patterns learned from the data. Two sources of online data collection for AI applications are:
1. Web Scraping: Collecting data from websites, blogs, or forums to gather text, images, and other information.
2. Public Datasets: Many organizations and institutions publish datasets that can be used for training AI models, such as Kaggle, UCI Machine Learning Repository, and government databases.