How exactly do I go about extracting features from timestamps for machine learning?
A common approach for time-series classification problems is to divide the continuous stream of data into samples of a certain duration.

This is called sliding window segmentation.



You don't really use timestamps as features because they wouldn't be useful during the classification of unseen data. Imagine training a model with data obtained in 2018, and trying to classify data for 2019. The information is not on the dates but in the values of the other features!