Working with Data which is not Normal/Gaussian What happens if my data/feature is not normal? Can I still use machine learning algorithms to utilize such data for predictions?

I noticed in many data sciences courses, there is always a strong assumption of using a normal/Gaussian data. I have always wonder why this is so, and most people would say that due to central limit theorem, the data is always assumed to be normal.


However, what if the data that I am dealing with is not normally distributed?
Should I perform log/ exponential transformation on the data for the sake of getting normal distributed data?
Why is Gaussian data always best suited?
There are models that do not make assumption that the underlying data distribution is a normal distribution. 

For example, support vector machine just cares about the boundaries of the separating hyperplane and do not assume the exact shape of the distributions. Decision tree models also do not make such assumption.

Gaussian distribution is popular and it has been analyzed frequently for its simplicity but there are other models as well. 

If you know the distributions that you believed your data can follow, you can also build your own model, for example by maximizing the likelihood or posterior distributrion.

If you are able to convert your data to normal distribution and use a model that you are familiar with, you can try it and see how does it perform.