Also to add to that most of the work in ML is feature engineering, data cleaning, testing and building pipelines which all require a good software engineering background.
I do a lot of the grunt work of getting the data sourced, cleaned and ready and am called the 'data wizard' and other such annoying names.
What's frustrating is I can run the last lines of code and read and understand the output of the last step, but as the original question asked, management would prefer someone with a phd or masters in customer analytics to be the expert of the data output.
I'm not trying to learn about ML for purposes of employment, It's somewhat relevant to my current job, and I may have some interest in using it on my personal projects. But mostly I'm just learning for the 'fun' of it :)
I don't have the time, money, or inclination to pursue a MS in data science atm (My current 'formal' education consists of a BS in Comp Sci and an MBA), but I may go back to school when the kids are grown, more for personal edification than anything else, however. A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37).
I agree that it is "high level" and glosses over (purposefully) the nitty-gritty details of the "black boxes" for the most part. I say this as someone who took the first incarnation of the course, which was known as "ML Class" in the fall of 2011, before Coursera came about.
Despite it being high-level, though - this is what one of my "classmates" was able to create, about halfway or so thru the course:
http://blog.davidsingleton.org/nnrccar/
In 2012, I completed Udacity's CS373 course (https://www.udacity.com/course/artificial-intelligence-for-r...).
Today, I'm currently in the second term of Udacity's Self-Driving Car Engineer Nanodegree (the current lesson I'm on actually is a part of CS373 - so it's a kind of review lesson for me - heh). I'm having a great time learning about more in-depth understanding and knowledge relating to self-driving vehicles. Much of the learning can be applied to other areas of ML as well (learning how to use and abuse TensorFlow and Keras, for instance).
> A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37).
Don't let that stop ya! My plan after finishing this Udacity course is to actually work toward getting my BS and maybe MS in Comp Sci. By that time, I'll be well into my 44th year of age. I don't know if any of this will lead to a different direction in my career, but that isn't something I am really worried or planning about. I'm currently happy with where my career is; it pays the bills and allows for some fun, too. But if it should lead in another direction, so be it! I figure having this knowledge can't hurt me as a employment candidate, and will likely be seen as a plus. Worst case scenario, it will make my hobbyist robotics projects more interesting.
I figure I have another 20 or more years in me doing software development (assuming it remains a career option, of course); I personally have met more that a few other developers that age or older who are still making a living at it. So I'm not ruling out the possibility of a lateral move toward something involving my knowledge of machine learning.
Good luck with your studies!
And of the few job listings I've seen, most have high standards (PhD or min. Masters, x years of experience) with old companies (banks, car companies).
What's funny is that a lot of people in my circle in Canada are actually doing work for companies outside of the country (U.S., China...)