So... I don't mind this. Learning via a toolset is an OK approach, in a lot of cases. I personally like it. Realistically, a big part of basic competency is familiarity with a functional toolset anyway. Some people will expand their toolsets later.
Also, if MS succeed in providing good learning tools as a way of creating demand for their services, this is cool too. Ideally AWS and others will continue to do the same, and there'll be an incentive to create and maintain free courses.
If you want a more general/abstract course, these exist too. This just isn't it.
Based on a quick glance I'd say the course wants to get you to a point where you connect to data, do a little cleanup coding, use it to build a model & then turn it all into a web service. It probably has a little bit of stats & ML "theory," but not a lot. This is useful if its useful to you. If you're mostly interested in theory, this just isn't for you. Try the big coursera course, maybe.
In my free time, I took the big Coursera course, and it is (was?) also tied into a proprietary toolset, GraphLab. They have "you can also do this in pandas" instructions on lab assignments, but the instruction and tools are aligned to GraphLab. I imagine the vast majority of students will not even consider using pandas.
Graphlab Create has since been open-sourced (as Turi Create) by its new owner, Apple. I didn't have a problem with the setup back then.
Stressing over toolsets is like stressing over your first programming language. It's counterproductive. You will do a lot better by finding a source of instruction you trust and a toolset you can work with, and then actually making something.
Quality instruction vastly outweighs the tradeoffs with learning a new language/api/etc.
From having skimmed the Microsoft course's offering, it looks like it would prepare a good student to understand and apply the algorithms to make models, but not to implement the algorithms themselves (which the Coursera course does do).
Which is fine! I could tell you how to implement elliptic curve cryptography and prove most of the mathematical underpinnings, but I have no intention of ever writing an implementation myself.
For a theoretical background, I particularly like the Shai-Shalev Schwartz book, though it’s not about deep learning. I’m told the Goodfellow book is good only in the sense that a better book simply doesn’t exist.
I like how comprehensive this is. The problem (as I've stated before) with Ng's course is he hand waves a lot of the required math away which concerns me. Specifically vectorization I remember constantly being surprised what was possible mathematically instead of 'getting it'.
That said, is there an AWS version of this track? Maybe Math/Spark/Scala/ML oriented?
I do like that it seems comprehensive, so I think I'll invest time into doing it.
At least from the other specializations ive seen I haven’t seen this yet...
Well you have to use some tool, and to teach a course everyone has to use a common tool, so what would you suggest? Your own favourite editor on your own favourite Linux distro I suppose? What if other students favourites are different? The class will spend its whole time getting their environments set up..
FWIW I’ve taken some of MS EdX courses and they use Python, Jupyter, R, RStudio... loads of open source stuff. See https://notebooks.azure.com for the typical environment they use
Then again, that's very similar to many of the expensive Microsoft-certified trainings I've seen, so honestly it's still an improvement.
Same happened on Google (AdWords, App Engine), Apple (XCode), Amazon (AWS), ...
(I didn’t take that course)
never mind, the small FAQ at the bottom links to the bigger real FAQ at https://academy.microsoft.com/en-us/faq/
there the section "How much will this cost" reveals:
"You must purchase Verified Certificates from edX.org as proof of the successful completion of courses in the Microsoft Professional Program. The cost for Verified Certificates varies by course. The prices are published in the course information on the edX site. You may always audit the course for free on edX.org, but "audit mode" does not provide MPP credit. Pricing is subject to change. Additional charges may apply."
this is clickbait advertising...
Oh its not that bad:
"Can I take the courses in the track and not sign up for the Verified Certificates of completion from edX.org?
You are welcome to audit any online course for free. However, the only way to receive completion credit toward an MPP learning path is to obtain a Verified Certificate from edX.org."
As in, it is exactly what it claimed to be all along? Maybe online courses are not for you if you need to be spoonfed?
I read some of the other commenters who claimed that the tutorials were more like teaching how to use Microsoft tools.
At this point I started visualizing "tutorials" on how to open and close their application, describing the GUI layout, the workbenches, ... which is not hard for me to imagine about microsoft. I simply did not feel like checking for myself, since I would risk wasting my time. Time better spent reading actual articles...
If you'd (re-)read https://news.ycombinator.com/newsguidelines.html and use this site as intended, we'd appreciate it.
However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math.
It'll help you see where the math you're learning is actually used, as you learn them.
Try deeplearning.ai first, then try this "math for ML" course.
That said, a wide foundation including a deep understanding of linear algebra is more useful than just covering the specifics relevant to AI.
EDIT: And this applies to everything. Ex: I've been playing the guitar for over a decade but have been struggling with music theory. I've recently tried to apply the little algebra I know to it to try and find structure. I've found some really cool articles, for instance , and it's helped quite a bit believe it or not !! For French speakers that like music, math, and algorithms, I highly encourage a presentation by Moreno Andreatta  where he closes the presentation by performing a "rotation around the Do of the Beatles' Hey Jude".
She said that the first hour or two were a little frustrating for the kinds of reasons you mention. After that, she could skip the "long" explanations & examples by going to the math directly. You'll progress 5X faster than normal after that.
She "got it" by creating ML "proofs" for math problems without doing math. She didn't like all the image recognition examples. Said it's a confusing place to start.
You are probably stuck with learning from a source that assumes you don't know math, because most people (including me) don't. This course is probably not for you if you want theory, as it's focused on "using" ML algorithms, not writing them. That said, this might be useful to you if you already have stuff that you want to do, and need a way of doing the "sticks and duct tape."
It's sometimes offered concurrently with on campus sessions, and it is not a watered-down MOOC or a tutorial for some company's toolkit.
For something quicker & less applied, maybe the 3b1b series on neural networks: https://youtu.be/aircAruvnKk
You jumped straight in at the 8th course in the programme and are complaining that it assumes you know something?
So many people on this thread looking for any nitpick they can, we get it guys, you just hate Microsoft. This isn't for you, then, no-one's pointing a gun at your head and saying, take this free course.
EDIT: the more I think about this, the more I like the idea of a course about an adversarial approach to AI. They could name it something like "Abusing public machine learning services for fun and profit". And given the previous successful work on Katawa Shoujo, I really believe that 4chan could pull this off.