Published on July 23, 2019 by

I’ve designed an ordered reading list of free books to help anyone learn machine learning efficiently! I’ll explain why I chose each book and detail how it fits into the larger learning path. We’ll start by learning about how the original learning algorithm (the human brain) works, then progressively move into more difficult technical subjects in machine learning. By the end, i’ll have you learning about the cutting edge of machine learning (causal reasoning) and ideating as to how quantum machine learning will help drive the field forward. From beginner to doing cutting edge research in 10 books, enjoy!

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Book List:

Book #1: Incognito: the Secret Lives of the Brain by David Eaglemann
https://fatimekerimli.files.wordpress…

Book #2 – How Smart Machines think by Sean Gerrish (sign up to scribd for free, download the book, then cancel your trial so its free)
https://www.scribd.com/document/40421…

Book #3 – The Hundred Page Machine Learning Book by Andrej Burkov
https://github.com/ZakiaSalod/The-Hun…

Book #4 – Python Machine Learning 2nd Edition by Sebastian Reschka
https://github.com/rasbt/python-machi…

Book #5 – Grokking Deep Learning by Andrew Trask
https://github.com/ontiyonke/Free-Dee…

Book #6 – Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson
https://github.com/CamDavidsonPilon/P…

Book #7 – Doing Data Science: Straight Talk From The Frontline by Rachel Schutt
https://github.com/SayantanMitra87/Da…

Book #8 – Reinforcement Learning by Sutton and Barto
http://incompleteideas.net/book/bookd…

Book #9 – The Book of Why by Judea Pearl
https://www.academia.edu/36682718/_Ju…

Book #10 – Quantum Machine Learning by Peter Wittek
https://doc.lagout.org/Others/Data%20…

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