The list is too long to include them all. Every one of the major MOOC sites offers not only one but several good Machine Learning classes, so please check
[Udacity](https://www.udacity.com/) yourself to see which ones are interesting to you.
However, there are a few that stand out, either because they're very popular
or are done by people who are famous for their work in ML. Roughly in order
from easiest to hardest, those are:
* Andrew Ng's [ML-Class at coursera](https://www.coursera.org/course/ml): Focused
on application of techniques. Easy to understand, but mathematically very shallow.
Good for beginners!
* Hasti/Tibshirani's [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): Also aimed at beginners and focused more on applications.
* Yaser Abu-Mostafa's [Learning From Data](https://www.edx.org/course/caltechx/caltechx-cs1156x-learnin...):
Focuses a lot more on theory, but also doable for beginners
* Geoff Hinton's [Neural Nets for Machine Learning](https://www.coursera.org/course/neuralnets):
As the title says, this is almost exclusively about Neural Networks.
* Hugo Larochelle's [Neural Net lectures](http://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghA...):
Again mostly on Neural Nets, with a focus on Deep Learning
* Daphne Koller's [Probabilistic Graphical Models](https://www.coursera.org/course/pgm)
Is a very challenging class, but has a lot of good material that few of the other.
Theory-heavy tough course but in the long run, really worth it.