Learn Machine Learning

Everyone in the world should have access to high-quality machine learning resources. This to empower Free and Open Machine Learning.

This blog is an excerpt of the FOSS ML Guide. An opinionated list of great open access machine learning learning resources. A lot of garbage is produced on the internet and even paid courses are often not good or simply learn you garbage.

This list of open (Creative Commons licensed ) machine learning training resources contains resources for starters who never want to do ‘hands-on’. But it contains also resources for experts to get better and better. Never stop learning.

Other valuable open access ML resources:

• A Course in Machine Learning, http://ciml.info/

• 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 Learning, https://iamtrask.github.io/2017/03/17/safe-ai/

• Collection of Interactive Machine Learning Examples, https://aihub.cloud.google.com/s?category=notebook

• Cryptography and Machine Learning, Mixing both for privacy-preserving machine learning, https://mortendahl.github.io/

• Explainable Deep Learning: A Field Guide for the Uninitiated. Great learning guide for new and starting researchers in the Deep neural network (DNN) field. https://arxiv.org/pdf/2004.14545.pdf

• 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/

• Machines that Learn in the Wild – Machine learning capabilities, limitations and implications, https://media.nesta.org.uk/documents/machines_that_learn_in_the_wild.pdf

• 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

• Mathematics for Machine Learning, Garrett Thomas. Introductory class in machine learning from UC Berkeley(course CS 189/289A). See https://gwthomas.github.io/docs/math4ml.pdf

• 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

• Privacy Preserving Deep Learning with PyTorch & PySyft, Tutorial with Jupyter notebooks based on PySyft library, https://github.com/OpenMined/PySyft/tree/master/examples/tutorials

• 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

• Scikit-learn User Guide, https://scikit-learn.org/stable/user_guide.html

• scikit-learn Tutorials, https://scikit-learn.org/stable/tutorial/index.html

• Seeing Theory, A visual introduction to probability and statistics. Interactive learning book that visualizes the fundamental statistical concepts, https://seeing-theory.brown.edu/

• 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/

• TensorFlow, Keras and deep learning, without a PhD, https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0

Updates for this opinionated list of high quality open machine learning resources are welcome! Mail me or create an issue with your suggestion in the FOSS ML guide.