ML Learning resources¶
Learning machine learning does not have to be very expensive or time consuming. Great learning material for machine learning is licensed under a Creative Commons license. So in most cases also free available for anyone who is eager to learn this technology.
In this section an opinionated list of great machine learning resources is published for learning this technology. Of course only resources that are open, so only resources published using a Creative Commons license (cc-by mostly) or other real open license are included. So all references are open access resources.
Most learning resource include hands-on tutorials. So be ready to use a notebook, but most tutorials offer notebooks ready to use directly.
- A Course in Machine Learning, http://ciml.info/
- Advanced NLP with spaCY, https://course.spacy.io/
- AutoML: Methods, Systems, Challenges, https://www.ml4aad.org/wp-content/uploads/2019/05/AutoML_Book.pdf
- Building Safe A.I., A Tutorial for Encrypted Deep Learnig, https://iamtrask.github.io/2017/03/17/safe-ai/
- Collection of Interactive Machine Learning Examples, http://tools.google.com/seedbank/
- Cryptography and Machine Learning, Mixing both for privacy-preserving machine learning, https://mortendahl.github.io/
- Dive into Deep Learning, An interactive deep learning book with code, math, and discussions, https://d2l.ai/
- Foundations of Machine Learning, Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning, https://bloomberg.github.io/foml/#home
- Interpretable Machine Learning, A Guide for Making Black Box Models Explainable,Christoph Molnar, https://christophm.github.io/interpretable-ml-book/
- Machine Learning Crash Course with TensorFlow APIs, https://developers.google.com/machine-learning/crash-course/ This is a great course published by Google’s. It is advertised as a ‘A self-study guide for aspiring machine learning practitioners’
- Machine Learning Guides, Simple step-by-step walkthroughs to solve common machine learning problems using best practices , https://developers.google.com/machine-learning/guides/
- Mathematics for Machine Learning, https://mml-book.github.io/ Examples and tutorials for this book are placed on: https://github.com/mml-book/mml-book.github.io
- NLP concepts with spaCy ,Allison Parrish (http://www.decontextualize.com/ ), https://gist.github.com/nocomplexity/b7c4c0aa5a0b53f4f5ff1c4784084be6
- Practical Deep Learning for Coders v3, https://course.fast.ai/index.html
- Python Machine Learning course, https://machine-learning-course.readthedocs.io/en/latest/index.html
- Rules of Machine Learning: Best Practices for ML Engineering, cc-by licensed ML course developed by Google, https://developers.google.com/machine-learning/guides/rules-of-ml
- Spinning Up in Deep RL, become a skilled practitioner in deep reinforcement learning, https://spinningup.openai.com/en/latest/index.html
- The Elements of AI, learn the basics of AI, https://www.elementsofai.com/