#100 overall
Machine Learning
Udacity
4 recommenders · 4 threads · recommended in 3 years, 2016–2020 · score 4.5
Udacity's Machine Learning course, a set of online lessons introducing ML methods and practice, often mentioned alongside its sibling Udacity courses in AI and data science. Commenters describe it as hands-on and well segmented.
Commenters call the Udacity ML and AI courses too good for the price of nothing and a practical, hands-on way to start with machine learning.
Recommendations by year
20062026
I highly recommend the Udacity courses on machine learning. They even have ones on how to become a self driving car engineer.
I think it would be useful to take Udacity’s machine learning class as well to provide an additional perspective. There are some parallels such as edge detection uses the same techniques as convolutional networks, regularization is a general technique useful for both neural networks and clustering, optimization of steps in planning probably has a parallel to how Alpha Go works. Particle filters was cool.
Also, take several of the classes in the specialization. Don’t stop at the first course. Convolutional networks has been great.
I liked working on the notebooks and watching the interviews with some of the pioneers of Deep Learning.
AI != ML
For AI, I would take the Udacity AI courses.
For ML, I would take the Udacity ML courses.
I take a lot of different online courses, I have no affiliation with Udacity, but their courses are just too good.
I studied AI (focused on ML) in a decent grad school (and I like to think I had the best teachers there), and I think the quality of the courses is comparable.
Hey, I signed up just to reply! If you want to begin to understand the stuff for fun I recommend the Udacity cluster of data science courses - intro to data science, machine learning, and eventually deep neural networks. I took AI at Stanford and learned a TON but for getting your hands dirty fast the Udacity courses are more appropriate. They have a bunch and they are well segmented so if you want to divert into e.g. data visualization, there's a class for that.
Your learning will culminate in this course: https://www.udacity.com/course/deep-learning--ud730
First, one must understand that these things are not just over, but over-over hyped. It is a socially constructed meme that Deep Learning is only for real geniuses, math PhDs, etc, build by Deep Learning guys themselves.
The problem with AI is that one need enormous money and resources of a big corporation to produce results one could see on AI competitions. It their "research" they do progress, like everyone else, by trial and error (well, augmented with a decent heuristic search process) and the more people and hardware resources they could put in it, the more chances they will outperform other teams. It is resources, not "smartness".
I have completed the very first AI MOOC by Andrew Ng years ago - 780 out of 800 or something. The only difficulty is that it combines methods from math and programming, so one need to have some background to really understand hows and whys. Also really decent knowledge of English is required, otherwise one might miss the nuances in a very dense, loaded with terminology lectures, so, the entry level is quite high (of course, one could always copy-paste code without understanding and use ready-made toolkits and tutorials).
Apart from that it is nothing special. Basically, it is a function composition, with linear algebra and some numerical optimizations. When you have understood the basic building blocks - mathematical functions, processes and algorithms involved, there is nothing much else to do - one has to apply the theoretical knowledge which is, again, not a big deal, to real problems, and this is where big corps with resources took the advantage.
As long as you manage to get inside one of bigcorp AI lab, you become a star, simply because of the well funded PR machine of the corp. Everything that comes out is super cool, of course, so even being mentioned in the context makes one super cool too.
In my opinion, the guys from upper middle class families, who went through a decent technical school (with mom and dad's money) which taught them the basis needed for entering AI, are not that special. I probably could beat one or two of such snobs, having no high school education at all, never studied English or programming in a school and being raised in an impoverished family, but this is another story.
As for ML, take Andrew Ng's course, it is pretty accessible, and then take the one on the Udacity (with all these arrogant hot shots) and realize that there is really no magic in it. It is not that hard.
When you see a cool paper or video about some "breakthroughs" it ML, take into account that it is mostly due to resources spent on it, not some kind of extraordinary genius of the authors of the paper. Remember, all they do is basically a heuristic search and constraint satisfaction problems - train, test, change function (layer) composition (usually, without real understanding of whys), re-train, re-test. The problem is that there are very few of such slots in megacorps.
The guys like Andrew Ng himself are, of course, the real stars. But this kind of success comes from years of rigorous training similar to what Olympic champions have undergo. I personally don't think I have such ambitions or wish to embrace such lifestyle.
Peter Norvig's Artificial Intelligence:
http://www.amazon.com/Artificial-Intelligence-Modern-Approac...
Has plenty of examples in Python. You can also look at different Udacity courses. They have a couple dealing with ML with Python.