This is possibly the most outstanding university class you will ever take. It is definitely the best university level course I have ever taken, and I have taken quite a few, both in person and online (MOOC). If you have any interest whatsoever in how computers learn to recognize faces, text, or recommend movies you might like, this class is nearly perfect in every way.
The instructor is an amazing human being and as cofounder of Coursera cares deeply about education. Yes, you will have to do some programming, but the instructor assumes no previous knowledge and all the information you need is available online. You will probably need to plan to spend more time on this class than estimated if you are a newcomer to computing, but only due to your background, not because the instructor has not organized the material in the most efficient and convenient format possible. Unlike some other poorly-thought-out MOOC where you waste time looking for information or confused about what is expected, this class is extremely well organized and presented in a straightforward, humble manner. In fact, I would suggest that any professor wishing to teach an online MOOC class should take this class first to see how real teaching is done by a professional who really knows the material but is not trying to impress the students with his knowledge by confusing them unnecessarily.
This is not an easy class, but it is tremendously rewarding to complete. It will probably expand your mind a few IQ points.
It is a very well-balanced version of the course. Some time ago I tried watching the original Stanford video recording of this course and it was too dry with endless math derivations. On the other hand, this interactive Coursera version strikes the right balance between the theory and application. The course is very practical and you can build very useful systems just based on the material presented in the course. I've watched several similar courses, and this one is by far the best.
It was a great class. Supervised and non-supervised machine learning algorithms were explained really well as well as how to design, analyse, and tune the system. Programming assignments required writing couple of functions in the given scripts. Understanding linear algebra and some knowledge of Octave are nice to have for this class.
Great stuf, even if such a broad range of topics could be easily split in two more courses.
Explanations are clear. I had liked a way to use other programming languages than octave (i.e. Scala or Python)
All other Machine Learning courses require an advanced knowledge of programming, this one is not, and I really appreciate it as I have a background in statistics but not much coding experience . Great course, highly recommend to anybody who is interested in data.
Good introductory course on important topic for many businesses. ML is used in filtering spam, weather prediction, customer segmentation, Netflix recommendation, fraud detection, medical treatments etc. If you're interested in the subject this course will get you started with basic algorithms and implementation.
What amazes me the most is the ability that Andrew has to make complex theory something easy and fun to learn. This course was the very best one I've taken in Coursera and I really hope that Andrew will teach us a lot more in the near future!
This class was awesome. Prof. Ng has a fantastic way of getting complex ideas and concepts and bring them to a level that even a 6-year old kid can understand. For those in the fields of Data mining & analysis, this course is highly recommended.
I took the first session of this course in 2011. Great teaching of great subject that is the core for AI. Programming assignments are not very difficult, but having working knowledge of Octave or Matlab would be helpful.
Great ML course. It gets you started using algorithms of supervised and not supervised ML and builds an understanding how to choose the best algorithms are for your data/problem and how to look for an error.