The field of deep learning is moving really fast, and a lot of that research is available on arXiv. Andrej Karpathy built a tool that surfaces interesting papers: http://www.arxiv-sanity.com/
r/machinelearning tends to vote up significant work and news
And this list of papers may be helpful:
For total beginners, we created this page: https://deeplearning4j.org/deeplearningforbeginners
2) Read the abstract, intro, and conclusion and see if the problem and result are worth the effort.
3) Read the body. The whole body may or may not be relevant to you depending on your interest. If you are starting out in a field, try to read and understand the whole thing. You will learn lingo, what is expected of a published paper in the field, improve your math skills, and make reading other papers easier.
If you are having a lot of trouble with 1 or 3, find a MOOC relevant to the subfield and go through it then go back to the literature.
When people say "I don't do or have the opportunity to do ML at work" it's because they don't have data to analyze or actual things to program or do in order to gain the experience they need to make the videos worthwhile to watch.
If I were to watch 10 videos on ML, but not actually go write code or analyze data, then the videos aren't going to get me a ML job or be that helpful other than learning a little about the topic.
It's kind of like watching a open course on ancient history or some similar topic, but without writing a paper on it. Yes the video is interesting, but what gets me a job and experience is the thought and work that goes into the homework.
But even if these were just videos, it's a good resource.
One thought about that: If you don't have "work data" to work on, there's still TONS of other "stuff" out there that you can work with. And given how much Open Data / Linked Data datasets are out there, for many industries I'd bet there's a pretty good chance one could find some interesting analysis of one of those datasets that would have value for their employer. Digging in and building something like that could help with bridging into an ML role. Or, if it's obvious that it just Isn't Going To Happen with one's current employer, it's always possible to just build something cool around an Open Data dataset and plop it up on GitHub to show other people. And in either case, it provides the motivation of having a project to work on.
There are also Kaggle competitions.
Over 9000 github stars and 1,260 forks for a repo started on 9th October????
If you want the interactive system, please check "A Visual Introduction to Machine Learning:http://www.r2d3.us/visual-intro-to-machine-learning-part-1/"
The Math will be my most difficult challenge, and i'm starting to brush up / learn courses to hopefully enable the proper skillset. /fingerscrossed
My main source will likely be Khan, any sources you like more than Khan? Ie, class room / instructor style (class room is probably good, given the ease of testing math)
Once you have the motivation to learn something, because it solves an immediate problem you have, you will learn it.
I'm going to put in the work to learn it proper, but i suspect it will take me a fair bit of time. I've got many years to make up for.
Luckily there are many institutions online to aid in fundamental education like this :)