I’ve seen many people confused on where to start, what does what, so I’m going to try to sum up all the courses, what they go over, and where to start here in this post I’ll be going by when a course came out, from most recent to oldest:
This is the most recent version of Practical Deep Learning for coders, and follows the book extremely closely. If you’re new, start here as it also combines the ML course below.
This course comes after Practical Deep Learning for Coders. It involves building the fastai library from the roots. Extremely program heavy, only recommended after taking Practical Deep Learning for Coders and have at least 1-2 years of experience coding.
A top-down approach to Natural Language Processing taught by Rachel Thomas. This course goes over most of the machine learning techniques for NLP data, and how they all work. Covers everything from Topic Modeling to Sequence to Sequence models.
A top-down approach to machine learning, uses the most recent version of the fastai codebase (in the fastai repo). One year of coding is recommended, and the best intro to machine learning course I’ve ever taken. This course is a must take and a great starting place.
A second run of Rachel Thomas’ Computational Linear Algebra. Great for understand some of the background math and how neural networks begin to work. Videos are unavailable, I recommend watching the v1 of the course.
Focuses on random forests and some computer vision and utilizes an older version of the fastai library. Great starting point if you’re nervous about the jump to Deep Learning and Neural Networks. You can find this library in the ‘old’ folder in the fastai repository
I hope this helps someone with the somewhat confusing question of “What do I take?” If anyone has edits or improvements please add some! I’ve made this a Wiki