Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David
For practical purposes:
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd Edition) by Aurelien Geron (There exists a helpful github repository for this book)
Also you can consider:
An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Hi Sahil, looks like you've already gotten some great recommendation on reading material. Not sure what application you have in mind with ML, but we have a no-code auto-Machine learning platform made for biomedical research. And we're currently open to select group of beta-users. It's free for you to use/try if you're interested, just send a note to [email protected] with subject line: beta-user I'd also be happy to give you more details if you'd like. Cheers, Jane
Dear Jane Sun, thank you for your response. Basically I'm working towards applying machine learning algorithms to nonlinear fiber optic channel to facilitate nonlinear distortion free transmission. I saw SolverGen platform is a web-based machine learning tool in biomedical research. I'm pretty impressed with your impactful research. Keep up the good work. Cheers, Sahil.