As pointed out elsewhere, typically people use "frequentist" to mean "non-Bayesian," which is not particularly effective as a classification.

Reducing a frequentist model to a Bayesian one though it's not a pointless excercise, since it elucidates the hidden assumptions, and at least you are better aware of its field of applicability.

Did you google Bayesian Machine Learning, or search for it on Amazon?

Only after buying the book I have :/ Bishop though seems a lot interesting, thanks!

The more meta point here is to not let a worldview shut you out from potentially useful resources.

Thankfully, I'm learning ML for my own education, it's not something I need to practice right now.