SM
Jun 14, 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
Oct 15, 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!
By Shashidhar Y
•Apr 2, 2019
Nice!!
By Vyshnavi G
•Jan 23, 2022
Ossum
By Md. T U B
•Sep 2, 2020
great
By Subhadip P
•Aug 4, 2020
great
By Nicholas S
•Oct 7, 2016
Great
By 李真
•Mar 5, 2016
great
By Vemuri s s n s d s
•Jan 24, 2022
good
By VYSHNAVI P
•Dec 13, 2021
GOOD
By SAYANTAN N
•Oct 29, 2021
good
By boulealam c
•Dec 15, 2020
good
By Saurabh A
•Sep 11, 2020
good
By SUJAY P
•Aug 21, 2020
nice
By ANKAN M
•Aug 16, 2020
nice
By Dr S J
•Jun 19, 2020
good
By AMARTHALURU N K
•Nov 24, 2019
good
By RISHI P M
•Aug 19, 2019
Good
By Akash G
•Mar 10, 2019
good
By xiaofeng y
•Feb 5, 2017
good
By Kumiko K
•Jun 5, 2016
Fun!
By Maram A A
•Dec 31, 2022
🙏
By Arun K P
•Oct 17, 2018
G
By Navinkumar
•Feb 23, 2017
g
By MARIANA L J
•Aug 12, 2016
The good:
-Good examples to learn the concepts
-Good organization of the material
-The assignments were well-explained and easy to follow-up
-The good humor and attitude of the professor makes the lectures very engaging
-All videolectures are small and this makes them easy to digest and follow (optional videos were large compared with the rest of the lectures but the material covered on those was pretty advanced and its length is justifiable)
Things that can be improved:
-In some of the videos the professor seemed to cruise through some of the concepts. I understand that it is recommended to take the series of courses in certain order but sometimes I felt we were rushing through the material covered
-I may be nitpicking here but I wish the professor used a different color to write on the slides (the red he used clashed horribly with some of the slides' backgrounds and made it difficult to read his observations)
Overall, a good course to take and very easy to follow if taken together with the other courses in the series.
By Hanif S
•Jun 2, 2016
Highly recommended course, looking under the hood to examine how popular ML algorithms like decision trees and boosting are actually implemented. I'm surprised at how intuitive the idea of boosting really is. Also interesting that random forests are dismissed as not as powerful as boosting, but I would love to know why! Both methods appear to expose more data to the learner, and a heuristic comparison between RF and boosting would have been greatly appreciated.
One can immediately notice the difference between statistician Emily, who took us through the mathematical derivation of the derivative (ha.ha.) function for linear regression (much appreciated Emily!), and computer scientist Carlos, who skipped this bit for logistic regression but provided lots of verbose code to track the running of algorithms during assignments (helps to see what is actually happening under the hood). Excellent lecturers both, thank you!
By Amilkar A H M
•Nov 27, 2017
It's a great course, but the programming assignments are a little too guided. That is good, to some extend, as it allows you to focus on the concepts, but at the same time, it leave little space for actually practicing your coding skills. I know they said from the beginning that this course was not focused on the implementation of the algorithms, however, how are you going to be able to use what you've learned without knowing how to implement the algorithms on your own.
When it comes to coding, nothing replace implementing the algorithms yourself. That is my only complaint. Other than that, it's great. I loved it. The concepts were well explained and they covered a lot of material. I wish they had spent more time in certain topics, but I guess this is just an introduction. Anyway, take this course by any means if you have some programming experience and have little to no machine learning knowledge.