- 7 reviews
- 7 completed
This was a great course. Martin clearly has a deep knowledge of the subject matter (he should - he created Scala!), but he is also able to convey this knowledge to students in an easy-to-follow, incremental way. The course's greatest weakness was touched upon in the last lecture - although Martin began the course talking about how functional programming is great because it's easy to take advantage of parallelism, this isn't touched on in the course. I'm excited to hear that he's planning a follow-up course, and will definitely take it once available.
I'm rating this as 4/5, but I'm not doing the full experience. I'm sort of "auditing" the course - downloading the videos and watching them when I get a spare minute. I'm not doing the essays or participating in the discussion forum, but they are quite active. The production quality on these videos is quite high - the professor is on a sound stage with assistants on standby. I have almost no world history education, but the material is presented in an easy to digest way. He obviously knows what he's talking about - it would be interesting to take an in-person class with him.
This course was difficult but interesting. If I had more of a physics background, I think I would have taken more from it, but it was still worth my time (if only to prove to myself that I can still do math). I think the prerequisites were a bit understated. They mention linear algebra, but as in all math there are different levels of knowledge. I've done a couple of university-level linear algebra classes, but I hadn't touched on tensors. I also hadn't done as much with complex numbers as the course assumed, although I caught up by the end. Understanding the math, or at least being interested in learning it, is key here. There are other forums where you can learn the weird and wonderful ways of quantum mechanics (and to a lesser extent, computation) without having to do all the equations yourself.
I'm torn with this course. I definitely learned some R and a few tricks, but if the course had been longer it could have covered some of the motivations behind data analysis in more depth. As quick introduction to R, the course was actually pretty good. Some people on the forum were decrying the fact that the programming assignments relied on functionality that wasn't explicitly covered in the lectures. I think this added to the course, since it showed off how good R's documentation is (via R Studio, at least). I'm not sure if I'll be in the position to use R in the near future, but I'm keeping the videos around just in case.
This was the first Coursera course that I took, and the first MOOC I took that wasn't computer science. What I really liked about this course was the diversity of the topics covered. Forest fires, herd immunity, the value of diversity, economic theory - this course touches on them all, but still manages to be cohesive and structured. Not very difficult, but very interesting - definitely worth the time.
Compilers was one of the courses offered at my university that I never had a chance to do during my degree, so I was pleased to see it offered on Coursera. I found this course challenging, although I didn't have any Java experience (which was technically a prerequisite) and being a new father put some constraint on the amount of time I could spend on the homework. I did manage to complete the programming assignment, which was by far the most challenging Coursera programming challenge that I've seen. Since I took this course, I started learning Scala via Coursera, and I find myself analyzing the syntax and semantics in a whole new way thanks to what I've learned.
This is the course that got me interested in MOOC's - it deserves 5 stars just for that, but it was a great course as well. I took the initial offering of this course in Fall 2011, before Coursera existed. The material is interesting, covering a broad range of machine learning approaches. The programming assignments are reasonable if you have a computer science background, and would be much easier if you have experience with a data-based language like Octave, R, Matlab, etc. One nit, and this is minor. Several times throughout the course, Andrew mentioned that learning the material presented in the course would put you above most ML users in Silicon Valley. Now that I'm in a company that does machine learning at a very large scale (albeit not located in the Valley), I find this assessment a bit questionable - these people really know their stuff. Overall, a great course, and increasingly important in the era of big data.