Picking up ML (systematically)

If you're interesting in picking up ML in a systematic manner. I'd recommend going with the following set of text books:

Pattern Recognition in ML (https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_3?ie=UTF8&qid=1520274288&sr=8-3&keywords=bishop+machine+learning)
Hands on Machine Learning with scikit and tensor flow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_2?ie=UTF8&qid=1512843086&sr=8-2&keywords=hands+on+machine+learning)
Introduction to Probability (https://www.amazon.com/Introduction-Probability-2nd-Dimitri-Bertsekas/dp/188652923X/ref=sr_1_sc_1?s=books&ie=UTF8&qid=1512843296&sr=1-1-spell&keywords=introduction+to+probability+john+tsitskilis)

As a serious practicioner of ML, you're to know the majority of material in these text books.

Probability & Statistics

Introduction to Probability https://www.amazon.com/Introduction-Probability-2nd-Dimitri-Bertsekas/dp/188652923X/ref=sr_1_sc_1?s=books&ie=UTF8&qid=1512843296&sr=1-1-spell&keywords=introduction+to+probability+john+tsitskilis

I'd recommend starting with this and reading it in its entirety if you haven't taken any courses in probability and statistics. This materials underpins most of ML.

Probability Theory: The Logic of Science https://www.amazon.com/Probability-Theory-Science-T-Jaynes/dp/0521592712

Extremely well written and unusual. Deals with philosophical questions as well.

Statistical Inference https://www.amazon.com/Statistical-Inference-George-Casella/dp/8131503941/ref=sr_1_1?s=books&ie=UTF8&qid=1512843382&sr=1-1&keywords=Statistical+inference+casella

More advanced treatment of statistical inference.

ML

Pattern Recognition in ML https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_3?ie=UTF8&qid=1520274288&sr=8-3&keywords=bishop+machine+learning

This is a good book for getting started with ML

Hands on Machine Learning with scikit and tensor flow: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_2?ie=UTF8&qid=1512843086&sr=8-2&keywords=hands+on+machine+learning

Surprisingly good for a textbook that is supposed to be hands on. There are extremely clear explanations on various neural network architectures and tensor flow.

Learning from data https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069/ref=sr_1_1?s=books&ie=UTF8&qid=1512843162&sr=1-1&keywords=learning+from+data

This textbook is short and written exceptionally well. It's at an introductory level, but also has some advanced ideas that aren't covered in other textbooks.

Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/

Written really well and is available for free online.

Kevin Murphy https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_1?ie=UTF8&qid=1512843644&sr=8-1&keywords=kevin+murphy+machine+learning

Advanced ML -- This textbook is a bible of ML. Has a lot of useful material, but leave it for after you've got the grasp on basics.