Medium, 2017. — 97 p.
This guide is intended to be accessible
to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series. If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc. Take a look at our top picks in the Appendix: The Best Machine Learning Resources.
Supervised Learning.
Supervised Learning II.
Supervised Learning III.
Unsupervised Learning.
Neural Networks & Deep Learning.
Reinforcement Learning.
Appendix: The Best Machine Learning Resources.
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