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.
I’m suggesting that you read the books from cover to cover and not only for reference.
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
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: 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
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
by Pedro Domingos
Pag. 352
Clever Algorithms: Nature-Inspired Programming Recipes
By Jason Brownlee PhD
Pag. 438
Free book online
Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming
by Eric Matthes
Pag 544
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
- Python Anaconda Distribution
- Visual Studio Code
- SciPy Numerical Python, NumPy, Jupyter lab and notebooks, Pandas, MatPlotLib
- scikit-learn Machine Learning in Python
- Tensor Flow 2.0 Deep Learning
- PyTorch Deep Learning
- XGBoost One of the best Tree algorithms
- Kaggle competitions
- FastAI Courses and Lib
- Khan Academy
- Land on Vector Spaces: Practical Linear Algebra with Python
- Introduction to Numerical Computing with NumPy
- Tools - Math - Linear Algebra
- Tools – NumPy
- Tools – pandas
- Tools – matplotlib
- Eloquent Arduino - Microcontrollers Machine Learning
- GitHub MicroML - from Eloquent Arduino - SVMs
- Lex Fridman
- Siraj Raval
- Python Programmer
- TensorFlow
- Two Minute Papers
- 3Blue1Brown
- Microsoft Research
- Derek Banas Programming Tutorials
- Jeremy Howard
- The Artificial Intelligence Channel
- mathematicalmonk
- Getting Started with JupyterLab 2019
Best regards,
Joao Nuno Carvalho