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Would it be beneficial for me as a developer to take these machine learning courses? I took a course in the uni a while back and know the general techniques, but I'm not sure how it would help me in my career unless I'm doing some cutting edge work in the field or focusing on a machine learning career, in which case wouldn't I need to be pursuing a postdoc or something in it?

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Likely. Data analysis (of which ML is an important part) is needed in many places, from entry-level to top-level.

I am a data science freelancer and I mostly do projects for IT-dominated companies. First, I was surprised that such companies need some external help with relatively simple tasks; only later I discovered that top-notch performance in webdev (or even: algorithms) does not mean that someone is able to do simplest data analysis.

For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis.

When it comes to deep learning in particular - I don't know.

>"For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis."

I've been wanting to get into this field recently. Do you have more info about these openings, perhaps?

Now I don't track offers (I get contracts through recommendations/networking), so I may be not up-to date. My background is different (PhD in quantum physics), so for me stats/data/ML is simple, but software architecture, algorithms - not as much.

When 3 years ago I was looking for data science internships, most of interview were strictly in software engineering. (I got into a more data-analysis oriented.) Even when I applied to Google a year ago (and failed), all non-trivial questions where in software engineering (some with data-oriented paradigms, tough).

Look at https://medium.com/@rchang/my-two-year-journey-as-a-data-sci... - the taxonomy of "Type A Data Scientist" vs "Type B Data Scientist" is useful. You want to apply for the "B" or even - software engineer in a company which deals with data and is open to shifting roles.

Going back to the interviews: I see that the set of questions is entirely different. E.g. if the first question is "how to invert a binary table" or "how to test if a black-box number generator is fair". But sometimes it is not clear from the job opening.

EDIT:

If you are interested in my background: http://p.migdal.pl/2015/12/14/sci-to-data-sci.html

It's enough tasks where you need to have understanding of the ML algorithms/workflows/tools, and be able to implement production system that integrates them into real systems, generating value for companies. In many cases you need to have very good domain knowledge & software development skills in addition to understanding of ML. And in ML-related systems, the big part of implementation not ML itself, but a lot of supporting stuff (figure 1 from "Hidden Technical Debt in Machine Learning Systems" paper (https://papers.nips.cc/paper/5656-hidden-technical-debt-in-m...) quite useful for understanding).

I personally took several ML courses from coursera/udacity/edX, and they helped me when I decided to move to another group that works on the ML-related projects.

No. Honestly, no. Do it because you think it's interesting. Very few companies do deep learning (Google, Facebook, and Microsoft come to mind - it might be useful if you work at these companies already). That number will undoubtedly grow, but the vast majority of ML/data science positions deal with stuff like linear regression, PCA, logistic regression, decision trees, random forests, maybe svms/boosting if you want to get fancy. Take an ML survey course like Andrew Ng's course to learn about these. Also basic statistics/probability.

It sort of makes you wonder if we're looking at a future where something like 5 teams composed of 1000 of the top researchers each are going to build the premier ML systems, and those teams can then solve most generalized ML tasks.

Most other programmers wouldn't be able to contribute much value in a world that worked like this.

This perhaps mirrors how the chip market works, which similarly involves a limited number of researchers involved in advanced manufacturing techniques that are highly specialized and mostly a mystery to other people in the technology field.

But there may be a larger market to hire people who know how to use the tools they design.

The caveat is that in the long term, ML systems are generalized systems that function independently and won't necessarily always remain in the form of an "API tool" that traditional programmers will interface with.

Machine learning only going to be more and more relevant in the tech industry. Eventually you're going to have to deal with some sort of data analysis, just because there is little to no barrier from data collection to data analysis.

I'm not sure that a deep learning course would be a good first course. But an intro course on linear regression and basic probability/statistics would be worth looking into.

I'd recommend really mastering basic statistics if you aren't going to go all the way with learning data analysis. It's surprisingly subtle and more widely applicable to a broad range of careers.

If you can make the time than learning new things and taking courses is always a good idea. You never know where you're going to end up as a developer. Who knows you may end up changing the course of your career. Also machine learning and AI are all becoming big fields.

TBH, I like machine learning in terms of its applications, but I have no desire to go into the field in order to do research, or deal with statistics, etc. I would rather just use it as part of my software that I am building. To that extent, how helpful is it for me to take these deep learning type courses?

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