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Borys Zibrov profile image
Borys Zibrov profile image
8/10 starsCompleted
  • 9 reviews
  • 8 completed
5 years, 7 months ago
First of all, I didn't see a button to leave a review on course talk, so if you have the same problem as me, try link http://www.coursetalk.com/coursera /mining-massive-datasets/review. I found this course ... First of all, I didn't see a button to leave a review on course talk, so if you have the same problem as me, try link http://www.coursetalk.com/coursera /mining-massive-datasets/review. I found this course very challenging (about 10 hrs of work a week or perhaps more) and pretty much theoretic. You'll get tons of useful theory, and very little practice. All quizzes and finals are about doing math calculations, and not about coding systems that do the job. Of course, it would be very nice to have both but it would require more time. There was only one (!) programming assignment on LSH, which was absolutely optional. Content is superb. There's a free book available on mmds.org which is very cool. I talked about this course being a theoretic one above, but I've got to say that this theory is extremely useful (should be at least) in practice. Course deals with tough subjects, like what to do if you are implementing a recommender system for instance, and have lots and lots of data that won't fit into memory. How to avoid disk thrashing? This IS tricky, isn't it? And things like that. You know what statistics is, for sure, you know (or heard at least) SVMs and NNs and CF and stuff like that, but probably you didn't try to implement it on web scale, did you? :) For absolute beginners in machine learning (experience ~ none), this course might be useful as well (perhaps with more work to do), because brief introductions to the corresponding topics are given. Now, few BUTs. I can't give this course 5 stars, and I was doubtful about 4 stars even, because organization was terrible. Missing pages on coursera, missing links (i.e. to find a way to see exam results you had to guess the right link !!), course structure was not intuitive at all. I don't know why, but they changed the order of the videos completely, with some weird references to the future lectures all the time (only they didn't say it would be in the future lectures). Instructors are great dudes and know stuff they teaching about, but some of them just can't teach. Jeff Ullman is a cool guy, to be sure, but his lectures were particularly dull... how to put it: content was superb, but uninterestedness of the lecturer and droning voice just put me to sleep in no time :) In this regard I love Jure Leskovec the best, he seemed passionate at least. So, great content, organization was terrible, learned so much (but not in depth, because to learn those things in depth you need more than 10 hrs of work a week for 3 months, be sure), and lecturers were so-so.
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Rohan Kapur profile image
Rohan Kapur profile image
6/10 starsTaking Now
  • 1 review
  • 0 completed
4 years, 9 months ago
This course is probably one of the best data mining courses on Coursera, but it has a few flaws. Firstly, it's difficult. This is NOT a flaw, but something to note. It's a Stanford graduate level course t... This course is probably one of the best data mining courses on Coursera, but it has a few flaws. Firstly, it's difficult. This is NOT a flaw, but something to note. It's a Stanford graduate level course that, per the instructors, "has not been watered down at all". This results in many people complaining in the discussion forums that the course has grown "legs", and can be quite stressful. Advice to ANYONE taking the course: make sure you have a lot of prior Linear Algebra knowledge. It's much more rigorous than the Linear Algebra used in something like Andrew Ng's Machine Learning course. However, there's also a great book that's published with the course. It's a wonderful reference when you're stuck on something. With that being said, the quizzes are not hard. A lot of the quizzes are normally very short, and do not require much insight into the material. A lot of them are just simple mathematical calculations that are somewhat in the smaller processes of the algorithms in the lectures. On top of this, there's no programming exercises, which are usually helpful for gaining insight on these topics. I think, however, there are a few set of lectures which are extremely difficult to understand. Week 2 is renown on the discussion forums to be the hardest portion of the course, most specifically because Jeffrey Ullman (who is an amazing Computer Scientist!) is a very monotone, rather boring and un-motivational lecturer (he directly reads off a transcript). He doesn't give many examples and his lectures are VERY long (30 minutes at one time), which leaves things rather abstruse. Sometimes, his train of thought can be near impossible to follow. This is where the MMDS book comes in handy. Luckily, Jure and Anand are great lecturers who make things concise, easy, and fun to understand. The content is GREAT. That's definitely an important thing to note. The topics they explore are rather interesting, and relevant. But the course moves very fast. There's a LOT of content to go through, usually two chapters of the MMDS book per week. The lectures can be quite long, and it makes things rather tiring. I think the biggest problem with this course is the lack of structure. I don't know why I'm learning what I'm learning. I don't know how it even fits back into Data Mining as a whole, or what is Data Mining? I don't know how single sets of lectures relate to the next set (it seems as if they do not at all). Weeks jump from one to another without any coherence. Why or when I would I use this in my startup or project? How do these topics relate together? There's no coherence or underlying motif to the course. The videos within the weeks lack coherence too. There are times when material from past lectures are randomly brought back up, and it seems as if random videos introducing new topics that aren't even explained into depth until WEEKS later are inserted at random points in the course. I've been able to retain very little of this course. I find that, each week, I almost certainly forget the intricacies and sometimes even the bigger pictures of the past week. This reflects on the sheer volume of content and the lack of effective structure of the course. MMDS has been mentally stimulating, but definitely needs an overhaul.
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