I was doing machine learning but never dig into stats before. Then I tried to study Bayesian inference and regression by myself and finally I got what it really

I was doing machine learning but never dig into stats before. Then I tried to study Bayesian inference and regression by myself and finally I got what it really means and its importance. First I realised that ubiquity of 'likelihood' and 'likelihood function', then I realised it's just a way to parameterise the model parameters instead of input data. Then MLE is a way to get an estimate of maximum of that function, which is interpreted as the most likely setting to give rise to the data observed. I know it's not statistically correct but I think it helped a lot in my understanding of other methods....