I recommend Elements of Statistical Learning, free @ https://statweb.stanford.edu/~tibs/ElemStatLearn/

This book covers everything from simple regression and classification from the statistical side to things like gradient boosted decision trees and the like on the ML side with enough math to make sure you understand what's actually going on.

I should note, it doesn't touch deep learning, which is what I suspect most people interested in 'machine learning' without any background in stats want to learn about these days.

I would suggest "Hands-on Machine Learning with Scikit-Learn and TensorFlow" if you search for a practical approach.

You can find accompanying code here: https://github.com/ageron/handson-ml