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How to learn modern Machine Learning

A guide to the adventurer First of all basic machine learning can be simple to understand, but to understand it deeply and see were the rabbit hole goes you have to do some serious kind of study. The plan to enlightenment has multiple parallel path’s, but books, internet info, youtube video channels, free online courses, constitute the main pillars.

Books

I’m suggesting that you read the books from cover to cover and not only for reference.

Most important books

The first book that you should read is a cheap book but a very good one. It summarizes in a small amount of pages what is the bulk of machine learning. The Hundred-Page Machine Learning Book by Andriy Burkov Pag. 160

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron Pag. 856 Note: If you can only have one book read this one.

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition by Sebastian Raschka Pag. 748

Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas Pag. 548

Deep Learning with Python by Francois Chollet Pag. 384 Note: From the author of Keras.

Deep Learning with PyTorch by Eli Stevens, Luca Antiga Pag. 450

Data Science and Machine Learning: Mathematical and Statistical Methods by Dirk Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman Pag. 532

Good second line books

An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pag. 426 Free book online Note: It will also make you understand a little bit of R. And it's a really good book to introduce you to the mathematical rigor of machine learning.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition by Wes McKinney Pag. 550 Note: From the author of Pandas.

Machine Learning: An Algorithmic Perspective, 2nd Edition by Stephen Marsland Pag. 457 Note: You will understand how to program all those algorithms from scratch.

Data Science from Scratch: First Principles with Python 2nd Edition by Joel Grus Pag. 406 Note: You will understand how to program all those algorithms from scratch.

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning by Chris Albon Pag. 366 Note: A cookbook book of recipes.

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Sylvain Gugger and Jeremy Howard Pag. 350 Note: From the author of FastAI Course and Framework.

TinyML: Machine Learning with TensorFlow on Arduino and Ultra-Low Power Micro-Controllers by Pete Warden, Daniel Situnayake Pag. 520 Note: Machine Learning on Microcontrollers and embedded software.

Grokking Deep Learning by y Andrew Trask Pag. 336 Note: Helps you understand Deep Learning from the basics in a simple way.

Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville Pag. 800 Note: It has a good introduction to Linear Algebra, Probability, Calculus and Optimization. You will need it to understand the text because it is of a high mathematical nature. Made by some of the inventors of deep learning. Free book online

Dive into Deep Learning by Aston Zhang, Zack Lipton, Mu Li, Alex Smola Pag. 902 Free book online

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman Pag. 745 Free book online

Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce Pag. 318

Pattern Recognition and Machine Learning by Christopher M. Bishop Pag. 738 Free book online

Reinforcement Learning

Reinforcement Learning: An Introduction second Edition by Richard S. Sutton, Andrew G. Barto Pag. 552 Free book online

Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more by Maxim Lapan Pag. 777

Global view on Machine Learning field from several fronts

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos Pag. 352

Biological inspired algorithms

Clever Algorithms: Nature-Inspired Programming Recipes By Jason Brownlee PhD Pag. 438 Free book online

Programming

Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming by Eric Matthes Pag 544

Mathematics

Summary - Mathematics for Machine Learning by Garrett Thomas Pag. 47 Free book online

Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Pag. 398 Free book online

Engineering Mathematics by Prof Anthony Croft, Dr Robert Davison Pag. 1024

Higher Engineering Mathematics, 8th edition by John Bird Pag. 924

Linear Algebra: Step by Step by by Kuldeep Singh Pag. 528

Introduction to Linear Algebra, 5th Edition by Gilbert Strang Pag. 584 Note: There is a excellent MITOpenCourse from this professor that follows this book.

All of Statistics: A Concise Course in Statistical Inference by Larry A. Wasserman Pag. 538

Information Theory, Inference and Learning Algorithms by David J. C. MacKay Pag. 640 Free book online

Algorithms for Optimization by Mykel J. Kochenderfer, Tim A. Wheeler Pag. 520

Convex Optimization by Stephen Boyd, Lieven Vandenberghe Pag. 727 Free book online

Sites

Youtube Channels

Have fun!

Best regards, Joao Nuno Carvalho