I am trying to wrap my head around the difference between machine learning and statistics for predictive purposes and interpretability...

Is there a sharp difference between the two in terms of predictive power? I understand how machine learning needs to be first trained with data to later make useful predictions, etc. Some of the used algorithms are the same we find in statistics (for ex., linear regression).

Given a set of data (not extremely larger), how is linear regression done with machine learning different from the more traditional linear regression?