machine learning

in my final semester in college i panicked about my long-term employability and took a class called "probabilistic machine learning" where we derived a good amount of descent algorithms from first principles and implemented them by hand.

during my first month at my first full time job out of college i asked my manager to reimburse me something like $108 dollars for a DataCamp subscription, and i went through most Python classes related to machine learning in the catalog.

then about eight months into my job i asked my manager to shell out something like 200 dollars for a few manning books on machine learning that taught keras and tensorflow in detail.

here are some resources i found helpful. whenever you get a hot button topic like this there is always a lot of noise so i was happy that i picked a few books/classes and stuck to them im of course still learning every day

some books i found helpful

"practical"

Deep Learning with Python by Francois Chollet (i thought this was better than the fastai book but YMMV)

Elements of Statistical Learning (Springer) (i think the more basic chapters do an incredible job in bringing out nuances of the methods you've probably seen a thousand times and i learn something new each time)

"theory"

Mathematical Analysis of Machine Learning Algorithms by Tong Zhang

Probabilistic Machine Learning by Kevin P. Murphy