A dataset is the collection of examples you use to train or evaluate a model.
Each row usually represents one example or observation.
Each column represents some property or measurement of that example.
A house dataset might hold area, bedrooms, location and price.
Good ML starts with actually understanding and cleaning the dataset before touching a model.
A dataset is just a table, most of the time.
Every row is one thing you are learning about, like one house or one customer.
Every column is one fact about that thing.
The first job is always to look at it, before any model exists.
How many rows, how many columns, and what does each column actually mean?
Real datasets are messy in ways nobody warns you about.
The same city turns up written five different ways.
Prices carry commas, or currency symbols, or the odd negative number.
Dates arrive in three formats inside the same column.
Some rows are duplicates, and some are the same event recorded twice.
Missing values are everywhere, and blank does not always mean zero.
Sometimes blank means nobody asked, which is a completely different thing.
More rows usually helps more than a cleverer model does.
But more rows of the same mistake helps nothing at all.
Understanding a dataset takes longer than training on it, and that is normal.
One question on this card. Every option explains itself once you pick, right or wrong.
55 concepts in this module, then a graded test with every answer explained.