I want to further progress my education, but am very divided: masters are expensive and long (2 years in Barcelona).
Udacity seems more accessible(moneywise), and has also the advantage of being taught by world leaders in the field.
What's your opinion on the matter?
That being said in the SF Bay Area "tech bubble" no one really cares about degrees. They care about what you know and what you can do. That may not be true where you're looking for a job though. You should look at what real job postings are looking for and perhaps talk to a recruiter or two (if possible).
Anything from a real university is likely to provide you a lot more information and access, way more than you can ever get at Udacity. However if you can't afford the time/money, Udacity or similar learning courses may not be a bad option.
Full disclosure- I'm currently in that program and loving it.
Once you're accepted though, you'll have to complete two foundational courses within a year (with a grade of at least B) in order to continue to be eligible. This applies to all new students.
From my own experience, some of the courses are pretty easy, so getting a B or higher isn't difficult.
It's not that rigid or difficult and carries no real weight.
You'll get as much if not more from the free machine learning Georgia tech course with Tom Mitchell's book than the nanodegree.
As a follow up, I took Thruns robotic driving course and it suffers from being a purely software course. There are optional hardware projects but no imparted hardware instruction.
So I'd be especially leery of an automated driving nanodegree degree online.
There aren't any research projects, and you won't ever operate at the cutting edge (WaveNet!) at Udacity. It's like trade school for software, which is fine if you're highly motivated or just want to acquire some skills.
Besides doing specific research, the CS grad curriculum at most schools is hardly cutting edge either. Sometimes it lags quite a bit behind the real world because of faculty being more focused on their own research vs teaching.
However, such roles are very limited in the industry. Most jobs you will find expect you to be able to apply, combine and optimize existing algorithms to given real-world problems. This requires a different set of skills that are seldom taught at University (you're typically expected to pick up these practical abilities on your own).
Udacity's Machine Learning Engineer Nanodegree, like its other programs, is heavily project-based, and has been developed with feedback from industry partners in order to emphasize the skills and concepts that are most relevant for the vast majority of jobs that are out there. This focused curriculum allows people with limited time or a related background to efficiently get started in machine learning.
Keeping this mind, ask yourself what your ultimate goal is, what time constraints you have, and choose accordingly. There is no shortcut to success, esp. in a competitive and highly technical field like machine learning - whether you opt for a Masters degree or a Nanodegree, you will have to spend considerable effort building a strong public profile (e.g. by participating in Kaggle competitions, and working on additional projects) in order to make yourself stand out from the crowd.
Good luck!
Disclaimer: I work at Udacity, in case you didn't realize by now :)
The important thing is that you're able to learn and master the material and use it to create real value in the world. If you can do that, I don't think anyone will care how you came by it.
The great aim of education is not knowledge but action.
I don't know how expensive Master's degrees are in Barcelona, but GA Tech has a online Master's in CS for ~$500 per course [1], where you could focus on ML.
[0] https://www.coursera.org/specializations/machine-learning [1] https://www.omscs.gatech.edu/
This blogpost outlines what my plan and concerns were about the self-taught route:
http://cole-maclean.github.io/blog/Self%20Taught%20AI/
Since then, I've been asked why I ultimately pursued the formal degree route, and this was my response:
"Without a formal CS background, I was pretty skeptical about my chances of getting accepted, but I applied anyways. I was so skeptical, that I convinced myself it wouldn't happen and set off to teach myself. But I ended up getting accepted into a Masters program in Barcelona, and I couldn't turn down the opportunity. I love Barcelona as a city, the tuition is reasonable and the program was inline with what I was looking for - a larger focus on application with foundation in theory as opposed to full on theoretical research.
I chose to do the conventional degree because of the above, plus the allure of receiving a piece of paper that people respect. Regardless of my thoughts on the real value of conventional degrees, it's hard to argue against their societal credit.
I'm new to this industry and pretty young, so take everything I say with salt, but my main advice would be to just build cool stuff. Whether you do it at a university or through autodidactism (learned that one from the HN thread), just work on cool projects. My naive hope is that people will care more about stuff you can actually build over a piece of paper with your name on it - but it doesn't hurt to have both."
That was in response to a thread about this guys blog, which gives some further perspective on the self-learning route:
http://karlrosaen.com/learning-sabbatical/
I'd like to add, that I've since decided to do both. I'm using the curriculum I developed for myself with online courses to compliment my formal education from the master's program, which has been working well so far.
ps. If UPC is the program you're looking into, it can be completed in 1.5 years (3 semesters) instead of the full 2. The last semester is dependent on how long it takes to finalize your thesis. Also, if you have questions about the program (again, assuming it's UPC's), my email is available from the site in the first link.
*http://masters.fib.upc.edu/masters/master-artificial-intelli...