Further Reading

Machine Learning Courses

• Machine Learning on Coursera – by Andrew Ng, probably the most famous course on ML

• StatQuest Videos

• ML Course on Udacity – by Katie Malone and Sebastian Thrun

• Machine Learning for Beginners – by Microsoft

• Machine Learning Caltech – video lectures on many ML topics

• Deep Learning Playlist – Lectures by Professor Bryce

• Fast AI Course

• NVidia Computer Vision Course

• Andreas Mullers Scikit Lectures

• ML Course Notes

Books

• Data Science from Scratch - Joel Grus

• The Elements of Statistical Learning – Hastie, Tibshirani, Friedman; an excellent deep dive book

• Deep Learning – by Goodfellow, Bengio, Courville

• Algorithms

ML in Python

Here are sources worth visiting to train models in Python:

• Scikit-Learn Homepage

• Scikit-Learn Cheat Sheet

• Data Science in Python

• Code Sniplets by Chris Albon

• Scipy Lecture Notes

• Serverless ML - MLOps blog

Deep Learning Papers

• Famous Deep Learning Papers

• Awesome Deep Learning Papers – most cited papers until 2016

• Awesome Deep Vision

• Deep Learning Papers

• distill.pub

• Papers with Code

Calculus:

• 3blue1brown playlist on calculus

• All other 3blue1brown math videos

Probability

• Probability in Jupyter

• Probability EdX course

Linear Algebra

• Linear Algebra in 4 pages

• Linear Algebra from UC Davis

• Linear Algebra from Saint Michaels College

Podcasts

• The Data Show podcast by O’Reilly

• Data framed – from Datacamp, very accessible

• Talking machines – pretty heavy but with the top scientists as guests

• Partially derivative – discontinued but funny

• Linear digressions – good for certain things

• Talk Python to me – some really good episodes on particular packages

• Videos on core ML concepts

• The AI Dilemma