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Comment by joshgel | Hacker News Reader

I took both of these courses and recommend. Practical is the key word in their description that differentiates them from other courses. The focus is on getting you up and running ASAP, then explaining the details and variation, then finally introducing you to learning how to translate the newest papers into usable code. You're not going to get a PhD from the course, but it will get you started on real-life projects.

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You might get an MSc, however :P Half of my master's thesis was based on practical understanding and skills I learned from the first Fast.ai course. It's also inspired me to start a PhD in a more quantitative field to persue probabilistic models in medicine.

(Here's that's master's thesis if you're interested :) Warning, big PDF ahead: https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski...)

Same here! Still busy with my MSc thesis though because I'm too busy applying some of the other stuff I learned from Fast.ai on other projects ':)

How long did it take you to complete each course?

Do you have a recommended 'schedule'?

I have tried twice, but have not finished.

Try a schedule that keeps you on ~ two hours a day, focusing on at least one of the videos in the 14-video series. So: 14 weeks, paying attention to one video a week and whatever peripheral studies you choose to follow up on based on the ideas Jeremy pushes in his lectures.

Edit: also, find datasets online that fit in your interests. There's no better motivation than to solve problems in a domain that you're already familiar with. I grew up on a farm and built a classifier to detect diseases as part of my fast.ai course-work: https://t.me/shambadoctorbot

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