Tables are an intuitive input format for machine learning models. You can imagine each row of the table as an example and each column as a potential feature or label. That said, datasets may also be derived from other formats, including log files and protocol buffers.
tables are just very helpful way to look at data for machine learning modeling
Many datasets store data in tables (grids), for example, as comma-separated values (CSV) or directly from spreadsheets or database tables.
comma-separated values is what csv stands for i never knew that
Yes, ML practitioners spend the majority of their time constructing datasets and doing feature engineering.
as everyone has told me, 90% of ML is data
Data trumps all. The quality and size of the dataset matters much more than which shiny algorithm you use to build your model.
Data is extaordinarily important, literally one of the reasons im planning to go through all this now before i start going through my data, you can have a good loss function, but ultimately its all dependent on your data