Oct 16, 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!
Jan 25, 2017
Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses
By VIGNESHKUMAR R•
Aug 23, 2019
By Gareth J•
Aug 26, 2019
A good course to teach the key points.
By Ashish C•
Mar 31, 2019
more topics like deep learning, neural networks need to be introduced
By Neelkanth S M•
Apr 08, 2019
The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.
By Pier L L•
Mar 26, 2017
Nice course but I would have expected more techniques (SVM for instance)
By Virgil P•
Feb 18, 2018
The exercises/assignments are far too simple
By Tom L•
Oct 21, 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.
By Ziyue Z•
Aug 10, 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.
By Fengchen G•
May 19, 2016
The course content seemed to be rushed out, as a result, the quality is not as good as the first two.
By Ole H S•
Jun 16, 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.
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).
By Matt T•
Apr 12, 2016
Good, but overemphasizes niche software product (graphlab).
By Supharerk T•
Jul 06, 2016
All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.
By Divya B•
Jun 13, 2018
Pros: Absolutely fantastic theory explanations. Establishes solid fundamentals. Cons: The bugs in test/notebooks could have not been rectified with new ones. Demands searching in discussion forum every time. Would highly recommend for starters!
By Kumar B•
Oct 04, 2017
This course covers the basics of classification very well, but I would have liked optional sections on more advanced topics. Some of the quiz questions were a bit confusing. It would have been good if the exercises also dealt with unbalanced data sets in more detail.
By Dmitri B•
Jun 06, 2017
Theory Quizes are good, but programming assignment not so good for me.
By Ilan S•
Nov 23, 2016
The videos were pretty goods. But a bit too slow and easy. The assigments were ok, but too guiding. Also there were too much reimplementation of algorithm
Jun 19, 2016
Exercises for Scikit-learn users were not organised.
Course took too long to start
By Nitsan O•
Apr 25, 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.
By nazar p•
Jun 30, 2017
While courses 1 and 2 of this specialization were quite good, I find this one a bit sparse on content. I think this course could be easily compressed into 2-3 weeks instead of 7.
Dec 04, 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.
By Tu L P H•
Jun 28, 2018
Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.
By Oliverio J S J•
Jun 17, 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.
Feb 03, 2018
By Rohit J•
May 12, 2016
A lot of interesting parts of the course are available as optional and a lot of the difficult parts of the coding exercises are provided to you - the challenge is not there. :/
By Omkar v D•
Aug 14, 2018