Episode 10: Best Books to Study Machine Learning

Hello! And welcome to a new edition of the Data Science Now newsletter. In this session, I talked about the best books to study machine learning. You can watch the video recording here:

And if you prefer you can hear the podcast version here:

Remember that we will be live every Wednesday here at Linkedin, 8 PM CST :).

Here's a short recap of what I covered in the session:

This session is very tight to session number 5 where I talked about the math of machine learning and also gave some material on books for machine learning. Here it is:

Below, as promised I'm going to list the best books to study machine learning, but I recommend that you first study all the material shown in session 5. The list is divided in a way that is easier to read, but you can try your own system. If you want to read a short description on these books and my opinion please listen the podcast version or see the video of the episode :)

Disclaimer: All the book links are for freely available books, or the ones that are stored in university domains or something like that. I'm not the owner of any of the books and I don't have the copyright for them. Please consider buying a copy of the books to support the authors, and because having physical books it's still awesome.

The list below is like formatted like this:

• NAME OF THE BOOK

LINK TO THE BOOK

Most important books (in my opinion):

• Pattern Recognition and Machine Learning (Cristopher M. Bishop)

https://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf

• Machine Learning (Tom M. Mitchell)

https://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf

• Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)

https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Introductory books:

• An Introduction to Statistical Learning with applications in R (Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani)

https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf

• Artificial Intelligence: A Modern Approach (Stuart J. Russel & Peter Norvig)

https://github.com/yanshengjia/ml-road/blob/master/resources/Artificial%20Intelligence%20-%20A%20Modern%20Approach%20(3rd%20Edition).pdf

• The Hundred-Page Machine Learning Book (Andriy Burkov)

https://themlbook.com/wiki/doku.php

• Probability and Statistics (Michael J. Evans & Jeffrey S. Rosenthal)

https://www.utstat.toronto.edu/mikevans/jeffrosenthal/book.pdf

• All of Statistics: A Concise Course in Statistical Inference (Larry Wasserman)

https://www.ic.unicamp.br/~wainer/cursos/1s2013/ml/livro.pdf

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron)

https://www.lpsm.paris/pageperso/has/source/Hand-on-ML.pdf

• Big Data MBA: Driving Business Strategies with Data Science (Bill Schmarzo)

https://index-of.co.uk/Big-Data-Technologies/Big%20Data%20MBA%20Driving%20Business%20Strategies%20with%20Data%20Science%201st%20Edition%202015%20Wiley%20%7BPRG%7D.pdf

Advanced Books

• Bayesian Reasoning and Machine Learning (David Barber)

https://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf

• Deep Learning Book (Ian Goodfellow, Yoshua Bengio & Aaron Courville)

https://www.deeplearningbook.org

• Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman &Jeff Ullman)

https://infolab.stanford.edu/~ullman/mmds/book.pdf

• Pattern Classification (Richard O. Duda, Peter E. Hart & David G. Stork)

https://github.com/dazzz/patrec2015/blob/master/Pattern%20Classification%20by%20Richard%20O.%20Duda%2C%20David%20G.%20Stork%2C%20Peter%20E.Hart%20.pdf

• Deep Learning with Python (Fran?ois Chollet)

https://faculty.neu.edu.cn/yury/AAI/Textbook/Deep%20Learning%20with%20Python.pdf

• Mathematics for Machine Learning (Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong)

https://mml-book.github.io/book/mml-book.pdf

• Probabilistic Programming & Bayesian Methods for Hackers (Caneron Davison-Pilon)

https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents

Oldies but important

• Exploratory Data Analysis (John Tukey)

https://apps.dtic.mil/dtic/tr/fulltext/u2/a266775.pdf

• Programming Collective Intelligence (Toby Segaran)

https://axon.cs.byu.edu/~martinez/classes/778/Papers/GP.pdf

• Data Analysis and Regression: A Second Course in Statistics (Frederick Mosteller & John Tukey)

https://archive.org/details/dataanalysisregr0000most

This last one is not a book but a very important article by John Tukey. I talked about this in an article title "On Data and Science":

• The Future of Data Analysis

https://projecteuclid.org/download/pdf_1/euclid.aoms/1177704711

If you want to learn more about this make sure to see or hear the episode, links are above :). 

Always Remember:

There's no easy path, you have to practice, study, and if you want to know where you're going, you need to understand where you're coming from. Then you will rule the world.

Thanks for reading this, please subscribe and share this with your network, it would help us a lot :)

With love by the Closter Team:

Gabriel Erives, Héizel Vázquez, Eilén Vázquez, Favio Vázquez.