ML is almost entirely based upon math that's decades upon decades old. The only reason it's become such a big thing lately is that the hardware and data sets have finally caught up to the point where it's useful on a broad scale.

Sure, the research portion of the field has made a lot of strides since 2011 but for anything that's not PhD or research level stuff, Ng's class is perfectly up to date.

You'd be hard pressed to find a better class anywhere.

If you're speaking tools and frameworks, yes, but core data structures, algorithms, and paradigms change very slowly. Andrew's course focuses on the latter, basic ML techniques that have been used for years and will continue to be used for years to come.

As someone pointed out in an earlier thread, the actual course is more advanced than the Coursera class, and its lectures are also available online: https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

As well as the problem sets: https://see.stanford.edu/Course/CS229

They are not any more recent, though.

I really enjoyed this one: https://work.caltech.edu/telecourse.html

As stated in another comment, the basics haven't changed much. The libraries you will use have evolved though. My impression is that that is where the innovation has been.

All of the concepts are still valid since he covers the fundamentals.

This math has been around since the early 80s. Don't panic.

How about this one?

https://www.udacity.com/course/deep-learning--ud730

There's also the Geoffrey Hinton class on Coursera, although I'm not sure if additional sections of it are being offered per-se. But you can still enroll in it and watch the videos and stuff. I don't know if it's any more recent than ang's class, but it goes into more detail in some areas and covers slightly different topics. At worst, it's a good complement to the other ang class.

For Neural Networks, I'm currently working along with Stanford's CS231N: http://cs231n.stanford.edu/

But I wouldn't really be able to keep up with it if I hadn't taken Ng's Machine Learning course first. The basics it teaches aren't out of date at all, and there's lots of regular old ML stuff in there that is useful now and hasn't changed in the interim apart from maybe which library you might pick.