When to normalize or regularize features in Data Science How can I know when I need to use normalization and when regularization of features.
I know that when I have big difference between min and max values in some feature / column that i need to scale data, also if I use SVM or KNN or Clustering i need to scale the data.

But how can I know when to normalize and when to regularize the features?
How can I know that if I have for example 200 features and 10 000 rows?
Is there any way that I can plot my data and see what can I use?
Step one would be understanding how the algorithms you are using work. Certain algorithms work better (distance based usually) when scaled, others don’t (like random forest). Knowing how an algorithm works will go along way to understanding when (and why) you should scale your data.