Thread by @chipro

This thread is a combination of 10 free online courses on machine learning that I find the most helpful. They should be taken in order.

1. Probability and Statistics by Stanford Online
This self-paced course covers basic concepts in probability and statistics spanning over four fundamental aspects of machine learning: exploratory data analysis, producing data, probability, and inference.
online.stanford.edu/courses/gse-yp…

2. Linear Algebra by MIT

Hands down the best linear algebra course I’ve seen, taught by the legendary professor Gilbert Strang.
ocw.mit.edu/courses/mathem…

3. CS231N: Convolutional Neural Networks for Visual Recognition by Stanford

Theories are balanced with practices. The notes are well written with visualizations that explain difficult concepts e.g. backprop, losses, regularizations, dropouts, batchnorm
youtube.com/playlist?list=…

4. Practical Deep Learning for Coders by fastai

This hands-on course focuses on getting things up and running. It has a forum with helpful discussions about the latest best practices in ML. By @jeremyphoward and @math_rachel
course.fast.ai

@jeremyphoward @math_rachel 5. CS224N: Natural Language Processing with Deep Learning by Stanford

A must-take course for anyone interested in NLP. The course is well organized, well taught, and up-to-date with the latest research. Taught by the amazing @chrmanning
youtube.com/playlist?list=…

@jeremyphoward @math_rachel @chrmanning 6. Machine Learning by Coursera

Originally taught at Stanford, Andrew Ng’s course is probably the most popular machine learning course in the world. Its Coursera version has been enrolled by more 2.5M people as of writing.
coursera.org/learn/machine-…

@jeremyphoward @math_rachel @chrmanning 7. Probabilistic Graphical Models Specialization by Coursera

Unlike most AI courses that introduce small concepts one by one, this tackles AI top-down as it forces you to think about what exactly you're trying to learn when you say ML. By @DaphneKoller
coursera.org/specialization…

@jeremyphoward @math_rachel @chrmanning @DaphneKoller 8. Introduction to Reinforcement Learning by DeepMind

RL is hard, but David Silver is here to the rescue. This course provides a great introduction to RL with intuitive explanations and fun examples, taught by one of the world’s leading experts.

@jeremyphoward @math_rachel @chrmanning @DaphneKoller 9. Full Stack Deep Learning Bootcamp

Most courses only teach you how to train and tune your models. This is the only one I've seen that shows you how to design, train, and deploy models from A to Z. By @pabbeel, @josh_tobin_, @sergeykarayev

fullstackdeeplearning.com/march2019

@jeremyphoward @math_rachel @chrmanning @DaphneKoller @pabbeel @josh_tobin_ @sergeykarayev 10. How to Win a Data Science Competition: Learn from Top Kagglers by Coursera

Time to head over to Kaggle to get some experiences building a machine learning for your resume and make some $$$

coursera.org/learn/competit…