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Comment by eternal_intern | Hacker News Reader

I work as a Mechatronics engineer and I have an interest in AI. I've personally gone through a lot of the online resources out there: 1. Andrew Ngs Deep learning MOOC

2. Fast AI parts 1 & 2

3. The old Google Machine learning course

But, what next?. From my experience, this doesn't give you enough credibility to get you a job interview at even a small sized firm, let alone Google.

Don't get me wrong, I really appreciate all the fantastic AI learning resources out there. Its incredibly enabling, but I feel like I'm missing the point of this - Is it to enable people to start companies using AI based tech, and grow the google compute based ecosystem? If its to grow the number of AI jobs and eligible people for those jobs, I have doubts whether that's actually working, or am I missing something?

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There’s a misconception out there about the data science skills gap - the truth is there is a huge demand for highly skilled data scientists, a big demand for data and ml literate developers, and a moderate demand for entry level data scientists.

These resources from google and courses like Fast AI are great for getting devs up to speed so they can meaningfully contribute to data science projects - filling that big demand for data + ml literate devs, especially internally. They’re not designed to get people jobs (disclosure, getting people jobs in data science is what we do at thisismetis.com)

If you want to go deeper? The open source data science masters is a good set of resources[0]. The first few sections of Goodfellow’s deep learning book are a great crash course in ML math/stats theory[1]. Introduction to Statistical Learning is a staple in most people’s library[2]. There’s a glut of intro level data science content out there on the internet, but intermediate to advanced stuff usually means putting in serious effort or breaking out your checkbook and going back to school (whether traditional or otherwise).

[0]http://datasciencemasters.org/ [1]https://www.deeplearningbook.org/ [2]http://faculty.marshall.usc.edu/gareth-james/ISL/

I am under the impression that the courses are designed for EE/CS engineers to get familiar with the foundations of modern ML, but it's not sufficient education to work as a full time ML engineer.

I returned to grad school for ML two years ago, and even now I still struggle with some ML job interviews when it comes to statistics and theoretical questions that I've studied two years for. One particularly challenging part of ML interview is that it covers much more than a typical CS interviews that I'm used to. I had a ML engineer internship interview with a famous ML company recently, and I was asked about sorting algorithms, hashing algorithms, non-convex optimization techniques, gaussian processes and manually compute the jacobian of a NN for backprop on the spot.

Surely the easy answer is do something with your knowledge. If you feel you can apply it, then I would say it was useful.

I couldn't imagine reading 3 books on python, and wondering will I get an interview. The question should be, can I write a simple program. Measuring by can I get a job interview is asking the reverse question.

I mean, would you hire you? Can you solve a potential company's problems with your AI toolset.

I get what you mean. I've been applying the skillset to Kaggle problems, each of which I imagine contain multiple subproblems which companies might face. But kaggle standings, in my experience, dont seem to be too convincing a metric for job openings.

The problem with the MOOC ecosystem at the moment is there's no clear path forward with them. I'd have imagined the MOOC certifications solving this problem, but I feel networking plays a much bigger role in the job market rather than credibility.

The only exception I see is Udacity, which, by its pricing has created a limited pool of graduates, and therefore are valued much higher

Stay away from:

- MOOCs

- Udacity

- Kaggle

I'm not being facetious, this is my honest advice.

I'm not an expert, but I don't see academic courses here such as e.g. [1]. And I don't see books read such as [2]. Personally, I would follow those type of things as grads or undergrads would follow the same courses, and on top of that I'd do what you do.

[1] http://cs231n.stanford.edu/

[2] http://neuralnetworksanddeeplearning.com

I'm in Ngs course now and also half way through fast.ai. I'm also interested to learn whether these courses reliably lead to anything. I gather from Jeremy's comments in fast.ai that those who attend in person reliably get good jobs in the field. So networking appears to be the key. I wonder what can be done to improve the networking opportunities for people who take the online courses?

    > but I feel like I'm missing the point of this

There is no overarching point - each of the resources that you've listed have their own reasons to share educational content for free, it's up to you to use it as well as you can.

Well, you're right. The people sharing the resources are doing it for education's sake - for anyone that's interested. I think it's more accurate to say I feel locked out

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