# Why linear and logistic regression coefficients cannot be estimated using same method?

I read in a machine learning book that parameters of linear regression can be estimated (among other methods) by gradient descent, while parameters of logistic regression are usually estimated by maximum likelihood estimation.

Is it possible to explain to a novice (me) why we need different methods for linear/logistic regression. a.k.a why not MLE for linear regression and why not gradient descent for logistic regression?

You're confusing apples with oranges. That's ok, because they are both delicious.