SM
14. Juni 2020
A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)
SS
15. Okt. 2016
Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!
von Ragunandan R M
•17. Sep. 2018
Good overall course.
von 2K18/SE/035 A K
•11. Nov. 2020
content is complete
von Lim W A
•21. Nov. 2016
Learnt new things.
von Mehul P
•17. Aug. 2017
Nice explanation.
von gaozhipeng
•30. Juni 2016
good introduction
von Alberto B
•17. März 2018
Very good course
von Antonio P L
•30. Apr. 2016
Fantastic Course
von Anand B
•7. Aug. 2017
Great course!
von PRASAD N
•3. Dez. 2020
good course.
von Ayswarya S
•5. Feb. 2019
best course
von Alberto J L R
•12. Okt. 2017
Good Mooc
von Syamsul B
•31. Aug. 2020
Great
von VIGNESHKUMAR R
•23. Aug. 2019
good
von Serge B
•2. Juli 2016
good
von IDOWU H A
•20. Mai 2018
B
von Ole H S
•16. Juni 2016
First. I like these courses allot. They are pretty close to covering just what you need to actually do machine learning in the real world and not dive too deep into topics that have no practical value.
However:
This course was a bit too thin, the last 4 weeks of the course contained little in depth informations and seemed to brush over allot of different topics that could have contained more information. Although they where important topics the course could go more in depth on at least 3 or 4 of those topics. The last 3 weeks could have been a course on its own if properly explored. However the concepts are well enough covered to be usable in practice i belive.
The programming exercises where ridiculously simple. Everything was reduced to filling in 1 or two lines in a bigger function. I understand that the point was to see how these functions are made and that it increases our understanding of the algorithms already existing in packages like schikit-learn and graphlab. Also the content became a bit too repetetive (actually started in the second course but continues in this course). The time used on variation over the same topic in different models made it challenging to pay attention when the lecture finally came to a new point (brain fell a sleep while waiting for something new).
von Ryan M
•25. Aug. 2020
While I feel like I have a good theoretical understanding of the issues involved in classification, with an understanding of how the algorithms work and how to implement them, this course could have prepared me better to attack an actual problem by following a real case study through, showing me what steps someone with experience in attacking real problems would take in order to come up with a good classifier.
In particular, while a number of classifiers were presented, there was little to no discussion of the relative advantages and disadvantages of each algorithm. In what cases should I choose logistic regression? A decision tree or a boosted decision tree?
Finally, it seems that random forests and support vector machines are common classifiers, and this course did not cover them. I instead had to learn about random forests (a relatively simple concept that could have been included with the boosted decision tree content) from scikit-learn's web site.
von Ziyue Z
•10. Aug. 2016
Compared with the regression course, this course was a slight disappointment. 1. there is less material compared to the regression course. Maybe this is because classification concepts are more intuitive. 2. the slides are much less prepared. Some of the sides even re-use earlier lesson slides in the beginning as a "review", much like soap operas re-use scenes from earlier episodes as "memory recall" to fill air time. 3. the math is more handwavy compared to the regression course. Neither course are supposed to go in depth with proofs, but I felt the regression course was at the right level and this course degraded too far. Do note it's very possible that I'm biased because I have seen more of the material from this course than the regression course.
von Sunil N
•2. Mai 2020
Bit of skewed distribution of load of work. Like week 6 and 7 were extremely light (merely 1 hour work), while week 2 and 5 were too heavy for a week. Syntax errors in assignment notebooks kept the nerves active but can be bit frustrating for relatively naive or trusting candidates, who might end up spending a lot of time finding bugs in their own piece of code. Overall a nice experience. Covid and wfh situation is not allowing proper time for learning but reminders helped in meeting the goal. Thank you
von 오승윤
•3. Dez. 2016
Turi stopped working on SFrame (at least on Github), and SFrame does not supports Python 3. Expect some difficulty if you use other tools like pandas - the programming assignment completely assumes you use SFrame. Fortunately data of csv format is provided, so you can complete it anyway but again, don't expect a smooth ride.
Also the lecture tends to cover general concepts than mathematical details. I don't like it, but that would be a good point to the starters.
von Tom L
•21. Okt. 2016
Well, after the regression course, which I actually found interesting, the classification course doesn't look so good. The programming assignments are mostly pointless. The use of graphlab doesn't make it better. The info presented in this course is rather superficial. If you're entirely new to machine learning, you could find some value in this course. If not, go buy a good book.
von Oliverio J S J
•17. Juni 2018
At first the course seems interesting but, as it progresses, it fails to convey why these contents are important in the deep learning era. In addition, it seems quite obvious that some contents are missing; I suppose that they have been eliminated due to the same problems that forced the cancellation of the last specialization courses.
von Francesco
•15. Nov. 2019
The material is good, but the choice of using GraphLab Create is a poor one. It's not used in the industry and it's poorly supported. I had issues installing it both via command line and via the installer, so I ended up using the AWS machine. But that has it's own drawbacks, such as the slowness and the setup time.
von Nitzan O
•25. Apr. 2016
The course is interesting and well taught. The professor is very enthusiastic and it makes the course fun to watch. The problem in my opinion is that the content is too superficial. It's completely lack of mathematical background and the programming exercises are sometimes no more than copy paste.
von ANIMESH M
•4. Sep. 2020
The course is up to the mark but what i felt missing is about the coding . They didn't focus on implementation tasks simply gave the notebooks for the assignments.
Also S.V.M and random forest classifiers are missing.
From my side concluding all the experience , i will give a 6.5 out of 10.