During my undergraduate and graduate coursework I occasionally found the lectures a bit lacking and found that a good textbook would be invaluable for filling in these gaps. This is a list of the best textbooks I found for various courses I've taken throughout my academic career.

Quantum Mechanics


Favorite texts:
Introduction to Quantum Mechanics by David J. Griffiths
The classic QM introductory text.
A Modern Approach to Quantum Mechanics by John S. Townsend
This was the textbook used in my undergraduate QM course. It's a good text but I think I would have been better served if I had the Griffiths text to pair with it at the time.

Quantum Computing & Information


Favorite texts:
Principles Of Quantum Computation And Information: A Comprehensive Textbook by Giuliano Benenti et al
This is probably my favorite book on quantum information and computing. My undergraduate QC course used the text by Nielsen and Chuang but I wish this was used instead. The Nielsen book is a classic and commonly cited but I found the Benenti book to be much more accessible.
Introduction to Classical and Quantum Computing by Thomas Wong
My second favorite book on quantum information and computing. This book differs from other QC texts as it goes over classical computing concepts as well as QC ones. It was the secondary book for my graduate QC course but was by far the most helpful.

Statistics


Favorite texts:
A Student's Guide to Bayesian Statistics by Ben Lambert
This book saved me in my graduate Bayesian statistics course and is the best textbook I have ever read. It is incredibly lucid while remaining fairly rigorous and at the same time keeping your attention. Everyone who intends to write a STEM textbook ought to be required to study this book. When combined with the Youtube lectures Ben made to accompany the book it's an AMAZING introduction to Bayesian statistics.


Machine Learning


Favorite texts:
The Elements of Statistical Learning by Jerome H. Friedman et al
A classical and accessible text on ML and statistical learning.
Pattern Recognition and Machine Learning by Christopher Bishop
This contains more math but is also excellent.

Network Science


Favorite texts:
Network Science by Albert-László Barabási and Márton Pósfai
This was the book used in my graduate network science course. Pretty good book and it's freely available online.
Networks by Mark Newman
A bit more rigorous and in depth than the Barabási text.

Classical Mechanics


Favorite texts:
Classical Mechanics by John R. Taylor
This was the text used in my undergrad classical mech. course. It was ok but some of the problems are kinda dated. Like what in the world is a plum line??
Introduction to Classical Mechanics by David Morin
I haven't read much of this book but from a cursory look it seems like a better and more modern alternative to the Taylor text.