Data refers to any information or set of information that is gathered and processed to provide insights or support decision-making. It can include numbers, text, images, videos, and any other form of information.
Structured data refers to data that is organized and easily searchable in a specific format, such as tables, spreadsheets, or databases. This type of data is easy to analyze and process as it has a well-defined fixed schema.
Unstructured data refers to data that is not organized in a specific format, making it difficult to analyze and process. This data type can include text documents, social media posts, images, videos, and other types of multimedia content.
Data augmentation refers to the process of increasing the amount of data available for analysis by generating new data from existing data. This can be done by adding noise to the data, rotating or flipping images, or changing the brightness or contrast of images, among other techniques. Data augmentation is commonly used in machine learning to improve the performance of models and prevent overfitting.