By Ethan Berl from Princeton, New Jersey 6 months ago Completed

Wow. I am so impressed by this course. Of the 11 Coursera classes and 1 edX course which I actively participated in so far (most of which were in Machine Learning, Artificial Intelligence, NLP, etc.), I think this one might be my favorite. Unlike some of the other courses from Stanford or Berkeley which cover similar subjects (and are great as well), this course takes a much more practical/applied approach and examines the technologies used to actually perform these techniques as well as give a great overview of current methods. Nowhere else would I learn as much about state of the art techniques like HTM. Nowhere else would I get to play around with data on web scale using techniques like Map-Reduce. Nowhere else could I get as complete of a picture of the current methods for having computers simulate intelligence, make deductions, run logic based systems, use collaborative filtering, perform pattern recognition, etc. To get the same value as this course gave, at the least I would have to take courses on Information Retrieval, Natural Language Processing, Machine Learning, Artificial Intelligence and then do the hard work of tying the different subjects together. Obviously I think that this class was amazing.

The quizzes were good tests of if you had understood the content of the lectures. The homeworks were a little sporadic and seemed slightly poorly planned but they were challenging and most importantly, they actually taught you something rather than merely testing if you could parrot back what the lectures had just taught you. I had a feeling of accomplishment when I finally got my tf-idf calculation running in map-reduce format or when I implemented a Bayesian-net to play doctor at diagnosing disease. In terms of practical applications, this course blows every other course out of the water.

By Robert Komartin from Bucharest, Bucuresti 6 months ago Completed

[Edit] I have upgraded the rating following course conclusion and recap. In the end, it does provide a well-rounded overview of the field (as well as the materials I was so dearly missing before :-)).

Nice in its intention, but failing to go all the way because of:
a. the lack of course materials
b. the style of "just scratching the surface" - which implies it is good for informative purposes, but hardly enough for doing anything in practice.
c. the style of the homework - where students are left "on their own" regarding languages to choose, algorithms to use, basically everything a beginner would need to properly address such a topic.

By Jeanne Boyarsky from New York 7 months ago Completed (Partially)

I dropped this course at the end of week 6 (out of 8 or 9).

What I liked:
1) The *one* programming homework on map reduce.
2) The professors seemed to have an awareness of both in class and online students.
3) The quizzes had some different answers if you retried them.

What I was neutral about:
1) Some of the topics were too easy. That's true in some other courses too (I have a degree in Computer Science), but it is easier to skip to the relevant parts.

What I didn't like:
1) The English was poor. There were an excessive number of spelling/grammar issues. Some of which affected understanding or quiz questions.
2) Some explanations were fine. Others I had trouble with. If I hadn't taken the AI class (and learned this already), I never would have understood conditional probability from the lectures.
3) Since the class was offered in person too for grades, homeworks were heavily defended to protect the integrity of grades. To the point where you had no idea why you go a question wrong. (And that guessing/wrong answers were heavily penalized, but I don't care about the grades part.)
4) I was under the impression there would be more hands-on/map-reduce type focus and not so much math/Bayes networks.'

Why I dropped the class 2/3 of the way through:
1) MongoDb is offering a class starting tomorrow (https://education.10gen.com) which I'm sure will be more hands on!
2) There was a one week break (school vacation) and I lost momentum.
3) I should have dropped it last week, but was hoping it would get better.

By Marek Stój from Wrocław 7 months ago Completed (Partially)

Very interesting subject and the approach taken by the professor. However the course is rather chaotic and not well prepared.

By Sai from Tokyo 6 months ago Completed

This course covers various technologies behind big data companies such as Google and Yahoo. It puts higher priority on breadth rather than depth. Topics covered in the first 5 units include PageRank, basic machine learning, MapReduce, and storage infrastructures (Google file system, BigTable, etc.). There are 3-4 more units left that will be mostly about machine learning. The course is simultaneously offered to some institutions in India but online students will not get certificate. To me, the biggest downside of this course is that the instructor, who is affiliated with an IT consulting firm, is not willing to share the lecture slides with the students so far, claiming copyright issue or something. That led to some students' voluntarily taking screenshots of the lecture video, compiling them, and sharing them for other students. There was 1 programming assignment about MapReduce (in Python).

[Update on Nov. 9, 2012]
- The slides were released a week before the final, thus bumping up the rating a little bit.
- The course staff changed their mind to offer certificates to online students.
- There were 2 more programming assignments, one of which offered an interesting insight about how SQL can be used to compute probabilities in Bayesian networks.

By Equanimous Creativity from OSAA, Århus, Denmark 30 days ago Completed

I took this class first time it was offered at coursera. I think this class is the hardest to review, because the quality varies from wasting our time with detailed videos on simple things over mindblowing insights into the nature of the subject to advance stuff without any details.

By Yogesh Shenvi Kuvelkar 3 months ago Completed

I want to thank Dr Gautam for the great value he has passed on to us. I signed up for this course on Coursera but could not follow the exact course timelines and assignments. Right from the first week lectures I somehow had the feeling that every topic that was being discussed in the course would be a great help later on when you get to the details. Now after completing the course, I am glad to say that I was not wrong.

This topic is a hot one for any IT professional. This course has not only helped me to learn more on the core course topics but easily relate with other emerging technologies like cloud, etc

By Ram Ram 5 months ago Completed

You would be able to understand several components of Big data technology(LSH, Machine Learning, Information theory, Map reduce etc), how they are related to each other and how they fit in to the big picture. The course while giving an overview also helps you get your hands-on MapReduce, Bayesian Classifiers, TF-IDF etc., As mentioned by the Dr.Shroff, the homeworks go deep and test your understanding of the material. Unlike many online courses in this subject, you would have a sense of accomplishment after you complete every homework. If you are planning to build a career in Big data, this course will help you understand where you stand, what are the different paths that you might pursue and also apprises you the research being carried in that field. Thank you Prof. Shroff for such a wonderful course!

By Naseera Melethil from Tokyo, Tokyo 6 months ago Completed

Thanks Dr. Gautham for being a great teacher for such an excellent course in which great concepts were taught in a simple & logical way.

Really enjoyed each bit of it, lessons, quizzes, programming assignments & even the Final Exam. Really appreciate the instructors efforts for giving lights in to these deep concepts. Looking forward to the upcoming courses from Dr. Gautham,
Thanks again.

By Celeste Horner from Eugene, Oregon 6 months ago Taking Course Now

I am building an AI system. Professor Shroff's course provided exactly the knowledge I need to address the challenge of Big Data -- machine learning algorithms, map-reduce, Hadoop, etc. Thank you very much! I enjoyed the smooth and pleasant delivery of the lectures and continue to refer to the information in this course.

By Dario Bottazzi from Bologna, Emilia-Romagna 6 months ago Completed

A nice introductory class on web mining. The topic is quite broad but, notably, the class provides a nice picture of this emerging discipline.

By Gavin Conran from Portadown 7 months ago Completed

Web Intelligence includes search, logic, probability, statistics and machine learning and from a first glance can appear very confusing. More than just act as the ring that unites all these topics this course encourages you to think about the meaning of human and web intelligence.

By Ben Haley from Chicago, Illinois 7 months ago Completed (Partially)

[Edit] I upgraded this review from a 3 to a 4 star because the last set of lectures were so worthwhile. I think you can get a great deal of value from this class just by looking at the comprehensive overview in week 8 assuming you have some ml background already (e.g. you took Andrew Ng's course).

Really great subject and I would love to give this class four stars except that it has had some technical snafus related to the speech rate and homework administration. If Dr. Shroff puts a little more time into this it should be a great introduction to learning from large quantities of data using modern machine learning methods.