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. For starters but also people who are already more familiar with the key concepts.
This section presents an opinionated list of great machine learning learning resources. A lot of garbage is produced on the internet and even paid courses are often not that good. But most material released under an open license is of excellent quality. This list consist of very readable references and some great hands-on courses.
Only resources that are real open, so resources published using a Creative Commons license (cc-by mostly) or other types of real open licensed material is included.
Most learning resources 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/
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/
Dive into Deep Learning, An interactive deep learning book with code, math, and discussions, https://d2l.ai/
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
Fairness and machine learning, Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan, https://fairmlbook.org/index.html
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, 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