Thanks for sharing your learning path and congrats to your success. There are so many great resources out there that it's possible for anyone to become an expert. Unfortunately, people like you who are willing to put in the hard work seem to be the minority. All of your success is well-deserved and props to you. Just like you said, most gravitate towards the easy-to-understand videos and blogs instead of confronting their gaps in knowledge. I've had the same experience with giving advice - anything that looks like it requires focused work (solving textbook problems!) or is unsexy is readily ignored in favor of the latest hype demo or high-level framework.

> It was primarily driven by the need to solve problems for autonomous systems I was creating.

I wonder if your success also had something to do with the fact that you had a specific problem you were trying to solve. Did you feel that your specific problem put everything you were learning into context and made it easier to stay motivated?

> I wonder if your success also had something to do with the fact that you had a specific problem you were trying to solve.

As a side note, and I've found that this is one of the best ways to learn. Find a hard but obtainable problem and work towards it gathering all the knowledge you need along the way. What works for me is breaking down a project into a bunch of mini projects and so it becomes a lot easier to track progress and specify what I need to learn. That way even if you don't finish the project there's still a clear distinction of what you learned and can do.

I'm highly in favor of project oriented learning.

I completely agree with this. In undergrad, I majored in non-profit management. Every single course in major had a field work requirement. Grant writing class required us to work with an area charity and write them a grant application, which our professor graded as one of our assignments. Same thing for program evaluation class and the rest. In addition to learning the topics, I learned so much about how to work with real world teams.

> Did you feel that your specific problem put everything you were learning into context and made it easier to stay motivated?

Absolutely, having focus on finding better solutions to a single problem for multiple years certainly helped putting everything into context and staying motivated. That is the biggest problem I think, really learning this stuff like is needed to in order to solve new problems takes years of investment and just can't be done in a week or a month. Pretty hard to stay focused on something for that long without a goal and support of some sort.

The way that having the specific long term problem to solve really helped was always having that thread spinning in the back of my mind thinking about how something new could be applied to solve part of it and possibly trying it out. Also thinking about if certain approaches could even be practical at all.

I suppose that is probably fairly similar to grad school though.

Teaching isn't just about presenting the information to the student.

That's basically all you've done. Here, student, read these complex topics and at the end of it all you will have learned machine learning!

The art of being a teacher is much more nuanced. You (apparently) fail to present the material in a way that is accessible, relatable, and not overwhelming.

For example, the first thing you say to do is go read a college textbook front to back, and do all the exercises. And you're surprised that nobody has followed your steps?

Yes, you are right. I'm not trying to teach the the dozen or so people who have asked, only to lay out a progression with prerequisites similar to what I did for self learning. I certainly do not have time to be creating courses and problem sets or anything more than answering specific questions.

The AIMA book has a lot of open resources around it that I always mention including a full open course I believe, it should all be linked on the site. Although, I also mention that while it is probably not a good idea they can possibly skip it and go right on to the ML course. Both of the Coursera courses are complete with lecture videos and work presented in a very accessible manner including interesting projects.