The Elements of Statistical Learning by Trevor Hastie:
First book I got is widely known, recently updated, and supposedly a great foundational text. I’m currently taking statistics and data science courses, and sometimes the statistics /math can get quite complicated so I really wanted a sharp advantage over my peers by getting quite good at math/statistics. The book is written by stanford professors, and contains in depth look at supervised learning, kernel smoothing, trees, model inference, SVMS, ensemble learning, and undirected graphical models. I’m excited that I got my hands on this book and plan on taking this knowledge to then next level by applying it
Hands-on Machine Learning with Scikit-learn and Tensorflow by Aurelien Geron
This book is a great text to futhering my knowledge of scitkit learn and tensorflow. While the mathematical understanding will surely come from the previous book, this book will be more about application I think.
The Kubernetes Book: Version 2.2 – January 2018
A book on kubernetes. One of the most starred repositories on github, I acknowledge just how revolutionary this technology is for web-development, startups, and any large scale company using microservices. I learned a lot about this in my cloud-computing course, and figured this tech was here to stay so I bought a book on it which I think will come in handy.