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On this page I decided to list several books which I think are quite good and which I have either already read, or I am planning to read in the future.

1) "Mathematics for Machine Learning" - Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

The good thing about this one is that it is free for download.

MML book

I haven't read it yet (just started it) but I find it perfect for people who studied lots of math in university courses, but which have forgotten most of the details. This book seems like a great refresher. 

2) "Machine Learning - an Algorithmic Perspective" - Stephen Marsland

3) "Introduction to Probability" - Bertsekas, Tsitsiklis

I've read some parts of it. Also, I've watched the MIT course video lectures in great detail.

MIT Applied Probability course - video lectures

4) "Netty in Action" - Norman Maurer

I like Netty, I use it even but I don't fully understand it. So I want to learn more about it. It's the best Java-based framework for building high-performance networking applications.

5) "Think Python" - Allen Downey

This one I already completed reading. I find perfect for people who have experience with programming languages but are new to Python. It's not heavy and it introduces all the major concepts nicely.

6) "MS SQL Server 2012 - T-SQL Fundamentals" - Itzik Ben-Gan

T-SQL Book

I completed this one around 2014 or 2015. This one is a great book for SQL. It is for people who know how to write some basic SQL statements like SELECT, INSERT, UPDATE, DELETE but want to get better than that. The author is really a master of explaining complex concepts nicely and interestingly while not sacrificing rigor. If you want to be become better than 95% of the developers writing in SQL, just read this book and make sure you understand every single example.

7) "Python Data Science Handbook" - Jake VanderPlas

I only glimpsed at this one a few weeks ago. Looks a great book. It was published in 2016 but doesn't seem outdated at all. I should read it in full.

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