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 Anand B•
Aug 07, 2017
By Anjan P•
Apr 29, 2016
Excellent course that details important concepts in supervised classification. The programming assignments can be a little easy to complete (and consequently easy to forget later), but I believe it's a well paced course and the lecture material is at any incredibly accessible pace, with options for more advanced material.
One suggestion would be to include more papers for additional technical details in the lecture or programming assignments as you did with dealing with unbalanced data.
By Lim W A•
Nov 21, 2016
Learnt new things.
By Sah-moo K•
Apr 03, 2016
Decision trees and boosting were great.
Aug 19, 2017
Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning
By Daniel C•
Apr 25, 2016
This series is taught by Emily and Carlos. Course 2 was Emily and this course 3 is Carlos. Carlos takes a more practical approach by showing how things are related using pictures, trial and error, what happens when we do this vs. that. Emily on the other hand dives down into the math and actual facts. I feel Emily is more difficult overall - but once I got through it, I had a better foundation and intuition as to how things work and better overall understanding. So - giving this class 4 stars as compared to Emily's class that is 5 stars. I feel if they would mix it with Emily doing the math immediately followed by Carlos explanations it would be best. Finally - I don't feel this course on classification had as much content. We could've done more.
By Ahmed N•
Feb 23, 2018
Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality
By Aleksander G•
Apr 11, 2016
Just one comment about how the course could be improved: the assignments should be more hands-on with fewer pieces of code written in advance. I say this is even though I am not a skilled programmer. The assignments would be a bit harder, but also a bit more rewarding.
By Zebin W•
Aug 24, 2016
It covers many aspects in clustering and the assignments are very helpful
Mar 20, 2016
good course but too much easy, can be a good review.
By Alejandro T•
Sep 09, 2017
It's a really good course, really liked it
By Mehul P•
Aug 17, 2017
By Dawid L•
Mar 20, 2017
Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.
By Uichong D L•
Sep 17, 2017
Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.
By Jacob M L•
Jun 24, 2016
Very approachable material, given the diversity of classification algorithms.
By Hexuan Z•
Oct 06, 2016
could be more challengable homework!!
By Luiz C•
Jun 07, 2018
Clear, good engaging videos, good quality/complexity balance of exercises
Jul 01, 2016
By Michael C•
Apr 07, 2016
The course provides an overview on classification methods in machine learning.
The lectures are clear and easy to understand due to the quality of the slides and of the explanations.
The limit of this course lies in the assignments: too easy if done with the provided notebooks and tools. Sometimes impossible to do with different tools (the suggested machine learning package is free for educational purposes, but otherwise it needs a license).
By Marku V d S•
Dec 23, 2017
I loved the course. Carlos Guestrin is an excellent and engaging professor that really captivate me to work hard to accomplish the assignments.
I just suggest that the assignments should be divided into small pieces to be taken as long the week is accomplished. I felt bad some weeks that had a lot of videos to watch before the first assignment.
By Alessio D M•
Apr 17, 2016
The course is definitely high-quality and the topics are covered in a good way. I'm not giving 5 stars because I would have expected SVMs and neural networks. Mentioning the many different algorithms for learning decision trees would have been nice, without necessarily focusing on each of them in depth. An entire week spent on precision/recall seems a little bit too much, without touching other metrics like F-score. Overall though a very nice course for beginners, and it definitely gives a good sense of classification challenges and approaches.
By Alexis C•
Sep 29, 2016
wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]
By Rattaphon H•
Aug 13, 2016
The questions are hard to understand and ambiguous though their answers are easy.
By Dilip K•
Dec 21, 2016
Excellent course that I have already recommended to a couple of people. Only annoying thing is the continued inconsistency between the Graphlab version and other versions (I use sframe with python - no graphlab) - some of the instructions are less than clear and needlessly waste time.
By Kamil K•
Aug 31, 2017
Carlos (the teacher) is a fantastic guy, but for me the content of this particular course was too easy comparing to other courses in specialization (when Emily was mainly in charge). If you only look at tutorial videos duration, you will see that they are two times shorter than in remaining courses. And some of them is "very optional". But, that being said, it is still a well taught course.
I wish it'd had more advance content, then I could give full 5-star review.