Glossary term
What is Standardising (scaling)?
In Apex Flow Academy, "Standardising (scaling)" means: Rewriting a column so its average is 0 and its typical spread is 1, so big numbers do not drown out small ones. It is taught in Machine Learning With Python: From Zero to a Working Model. Related terms include One-hot encoding, Overfitting, Median and Underfitting.
- 1 course teaches it
- plain-word definition
The facts
What is Standardising (scaling)?
Rewriting a column so its average is 0 and its typical spread is 1, so big numbers do not drown out small ones.
Where is Standardising (scaling) taught?
Standardising (scaling) is defined in Machine Learning With Python: From Zero to a Working Model.
Which terms are related to Standardising (scaling)?
Next to Standardising (scaling) in the course glossaries: One-hot encoding, Overfitting, Median, Underfitting, Impute, Pipeline, Data leakage and Cross-validation.
One-hot encodingOverfittingMedianUnderfittingImputePipelineData leakageCross-validation
The hard questions, answered straight
Bold questions people really ask. Each answer comes from the course data or a settled Academy fact.
Do I need experience before I learn Standardising (scaling)?
It depends on the course. Each course that teaches it lists its own prerequisites, and all are listed at beginner level:
- Machine Learning With Python: From Zero to a Working Model: Basic Python: variables, lists, dictionaries, functions, and running a .py file from a terminal.