Machine Learning Books and Tutorials
Machine Learning Tutorials
Open Licensed Machine Learning Tutorials
Machine Learning Algorithm License: MIT
ML from Scratch License: Apache
ML from Scratch License: MIT
Homemade Machine Learning License: MIT
Machine Learning Experiments License: MIT
Free Machine Learning Tutorials
NTLK License: CC-BY-NC-ND (Note: consider replace by Gensim or Spacy)
Ipyton Notebooks of Andrew Ng License: -
Machine Learning Course with Python License: -
Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
Machine Learning Certification at Google
Machine Learning Books
CC-BY Machine Learning Books
Automatic ML License: CC-BY
Free and Open Machine Learning License: CC-BY
Machine Learning Canvas License: CC-BY
Free and Open Machine Learning Book License CC-BY
Advanced Applications for Artificial Neural Networks License: CC-BY
Artificial Neural Networks - Models and Applications License: CC-BY
Artificial Neural Networks - Architectures and Applications License: CC-BY
[Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers] Author : Mariette Awad, Rahul Khanna License: CC-BY
CC-BY-NC Machine Learning Books
Pablo Caceres Notes License: CC-BY-NC, Project Web, Github License: MIT
CC-BY-NC-ND Machine Learning Books
Machine Learning, Statistics, and Data Mining for Heliophysics, GitHub Link License: CC-BY-NC-ND
Applied Artificial Neural Networks License: CC-BY-NC-ND
A Brief Introduction to Neural Networks License: CC-BY-NC-ND
Reinforcement Learning: An Introduction, Second Edition License : CC-BY-NC-ND
Recurrent Neural Networks License: CC-BY-NC-ND
Free Machine Learning Books
Mathematics for Machine Learning with Tutorial
Proprietary Machine Learning Books
Machine Learning Engineering by Andriy Burkov
List of Machine Learning Books
Machine Learning Notes
Archive License: CC0
Machine Learning Seminar
About ML Books
If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.
If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.
If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.
If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.
If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)
For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."
Transformers
Machine Learning Tutorial
Scikit Learn Lincese: CC BY
Learn Deep Learning
Machine Learning
Machine Learning
ML Books
Last updated