A collection of ML related material that I've found to be helpful.

Links focused on understanding the implications of AI and how to positively shape its development
https://80000hours.org/problem-profiles/positively-shaping-artificial-intelligence/#why-work-on-this-problem

A good overview of ML concepts with practical examples in both Python and R
https://www.udemy.com/machinelearning

Andrew Ng's excellent introduction to ML
https://www.coursera.org/learn/machine-learning

Andrew Ng's excellent 5-course deep learning specialization
https://www.coursera.org/specializations/deep-learning

An excellent and practical AI course with a lot of focus on coding state-of-the-art models
http://course.fast.ai/

DataCamp's Data Scientist track
https://www.datacamp.com/tracks/data-scientist-with-python

Google's ML crash course using TensorFlow
https://developers.google.com/machine-learning/crash-course/

David Silver's 10 lecture series on Reinforcement Learning
https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ

MIT course of self-driving cars
https://selfdrivingcars.mit.edu/

Guide to deep learning
http://yerevann.com/a-guide-to-deep-learning/

The leading online community for data analytics and predictive modeling. Lots of competitions and examples.
https://www.kaggle.com/

A high level overview of machine learning goals, processes, and strategies
http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/

A hands-on and well-explained implementation of neural nets in Python
Backpropagation: http://iamtrask.github.io/2015/07/12/basic-python-network/
Gradient Descent: https://iamtrask.github.io/2015/07/27/python-network-part2/

Guide to how convolutional neural nets work
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/

CNNs Illustrated
https://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/

Gradient Descent for Linear Regression in Python and R
http://blog.hackerearth.com/gradient-descent-algorithm-linear-regression

Clear explanation of LSTM-RNNs
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Neural nets
http://deeplearning.stanford.edu/tutorial/
http://neuralnetworksanddeeplearning.com/index.html
http://www.ritchieng.com/neural-networks-representation/

ML and Data Wrangling Blog
https://chrisalbon.com/#machine_learning

Deep learning Blog
http://www.wildml.com/

Deep Reinforcement Learning Beginner's Guide
https://deeplearning4j.org/deepreinforcementlearning#

Reinforcement Learning code examples (supplement to David Silver's course) http://www.wildml.com/2016/10/learning-reinforcement-learning/

Two-part intro to reinforcement learning
https://joshgreaves.com/reinforcement-learning/introduction-to-reinforcement-learning/

Eight-part intro to reinforcement learning
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0

List of the best performing papers on various dataset standards
http://rodrigob.github.io/are_we_there_yet/build/#about

Berkeley AI Research (BAIR)
http://bair.berkeley.edu/blog/

Paperspace blog
https://blog.paperspace.com/

Machine learning library built in Javacsript (on top of tensorflow.js)
https://ml5js.org/

Good introduction to Deep Learning by Michael Nielsen
http://neuralnetworksanddeeplearning.com/index.html

The Deep Learning book by Goodfellow et al
http://www.deeplearningbook.org/

Reinforcement Learning
http://incompleteideas.net/book/bookdraft2017nov5.pdf

Hands on Machine Learning with Scikit-learn and Tensorflow
http://shop.oreilly.com/product/0636920052289.do

Deep Learning with Python (using Keras)
https://www.manning.com/books/deep-learning-with-python

Outlines of topics to study while learning machine learning
http://karlrosaen.com/ml/
https://elitedatascience.com/learn-machine-learning

Import AI
https://jack-clark.net/about/

Detecting drawn numbers using a CNN
http://scs.ryerson.ca/~aharley/vis/conv/flat.html

Matrix multiplication
http://matrixmultiplication.xyz/

Online Graphviz (useful to visualize decision trees)
https://dreampuf.github.io/GraphvizOnline/

Health data
http://www.tycho.pitt.edu/

Cats vs Dogs classification
https://www.kaggle.com/c/dogs-vs-cats/data

Stanford Street View House Numbers
http://ufldl.stanford.edu/housenumbers/

Fashion-MNIST
https://github.com/zalandoresearch/fashion-mnist