Tabular data refers to information organized into rows and columns, like a spreadsheet. Each row represents a single observation or record and each column represents a specific attribute or feature. Tabular data is commonly used to store and analyze:
- Numerical data: financial data, statistics, etc.
- Categorical data: product categories, customer segments.
- Date/Time data.
- Text data: product descriptions, customer reviews.
- Geospatial data: geolocations, maps.
- Binary data: images, videos.
- Mixed data: different data types, such as a mixture of numerical and categorical data.
Once structured and organized, data can easily be processed by computer algorithms and be used as input to train machine learning models.
Unstructured data refers to information that does not adhere to a predefined data model or is not organized in a pre-defined manner.
Image classification is a process in AI where an algorithm is trained to identify and categorize objects or scenes in images.
Data extraction using AI refers to the automatic identification and extraction of relevant information from unstructured or semi-structured data sources, such as text documents or images.