As someone who recruits and interviews ML engineers, specifically for computer vision applications, I only barely look at your educational background.

I look for actual projects you have contributed to, published research, OSS contrib etc... that shows you can actually build something deployable and robust (mod your experience). Why? Most ML researchers are terrible at actually deploying products using ML.

School work can be relevant if it is part of a thesis, or research effort but in that case it's really still just [goto contributions].

At the application/implementation level you won't be making a new version of eg. gradient descent (and if you are you shouldn't be in industry as that's probably* a waste of resources), you'll be implementing existing ML systems and optimizing parameters. The most important thing you should be able to do is identify sources of data, structure data inputs flow and manage variability for the data you are using for both training and classification.

This doesn't answer your question directly, but it answers the implied question: What skills should I have to be a professional ML engineer?

I don't have anything certain, nor have I seen anyone answer this certainly - though this question has come up often. Obviously MOOCs will eventually be the way - huge companies are getting behind the movement; courses taught by best-of-best (eg Thrun, Ng); sustainability, etc. But I don't think we're quite there yet - I'd give it 3yrs. A recurring answer by hiring managers and recruiters is that they don't (yet) respect nanodegrees, at the various companies they recruit for. A Masters is much more respected (and looks like the majority minimum required degree for a decent ML job; no need for PhD, good luck with a BS). One option I'm very seriously considering is Georgia Tech's online MS "OMSCS" https://www.omscs.gatech.edu. It's a legitimate accredited MS at $7k (more expensive than Udacity, but _much_ less expensive than most MS programs). TMK it actually uses some Udacity courses in lieu of actual courses - they're partnered (hey, it might actually just be a nanodegree disguised as a university MS). I think it's sort of a transition from academia-proper to MOOCs, and it's respected by employers. So that would be my personal recommendation.

I'm going to be doing a lot more research in coming weeks. I'm going to publish my findings to my podcast http://ocdevel.com/podcasts/machine-learning and maybe drop what I find here too. Hopefully there will be some more answers here to pool from.

I've done Udacity's ML and doing the SDC in free time.

Short answer: Yes - I would do it again.

ML (Mid 2016): Cost-wise you may be able to gather the similar quality of materials for free, nothing are too unique and in many cases additional intensive research are still required. But, the Udacity provides nice structure and helps you to keep motivated.

SDC (2016): Much better experience for students, the quality of materials are higher, and amount of support from the peer group are extraordinary. Just one of the five projects in the first term are more complex than the whole ML nanodegree.

Haven't tried the AI nanodegree myself, but because it's a part of the new batch of programs I believe the experience would be quite positive (closer to SDC).

If you will decide to proceed with self-learning path, here is a nice multi-month study plan: https://github.com/ZuzooVn/machine-learning-for-software-eng...

In any case I strongly advice to take a look into Andrew Ng's coursera courses and Andrew Karpathy's CS231n on CNN (http://cs231n.stanford.edu/) as a supportive materials.

Also, if money is the main concern, you may want to apply to sponsorship, or maybe discuss this with your current employer.

Feel free to PM me on twitter (same nick) if you have any other questions, would be glad to assist.

I'm in the AI Nanodegree now and I have a Udacity Data Analyst nanodegree.

I also hold a traditional Engineering Masters and Engineering bachelors from colleges so I have that perspective as well.

You can read my take on the AI Nanodegree and other programs here.

http://canyon289.github.io/DSGuide.html#DSGuide

In summary I think Udacity is the best value per dollar for education but you can't rely on a Nanodegree, or even a regular degree, to get you a job. Like mentioned below most people care about your practical work. Udacity helps you get there but it doesn't get you all the way. But I think it's still worth it. Plus Udacity is pretty cool and offers scholarships which is relatively unheard of in this MOOC space as far as I know

I'd say only marginally. Even actual degrees are worth less than your portfolio of your actual works. "Non-accredited" certificates like nanodegrees (as opposed to "real" certs like CCIE that companies take seriously) can give you an edge when you say you have a certified coursework if you're pitted against someone who is similar to you. However, it won't be much of a leverage against someone more matched for a job or more experienced than you.

Does anyone that has taken the Udacity self driving course have comments on the 2nd and 3rd modules?

There isn't as much information available on those.

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