Does anyone have a cheatsheet that describes what data processing is needed for particular algorithms?

I am referring to normalisation, scaling, standardising, etc. I have some knowledge of this but I am aware that some ML algorithms and approaches need certain pre-processing steps while others do not (e.g. gradient boosted trees can handle NAs).

I am curious if anyone has a cheatsheet or resource that explicitly lays out what data tranformation and pre-processing steps are required for the majority of algorithms?

algorithms cheatsheet data data processing datascience processing