Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington
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Top-Bewertungen
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 :)
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!
476 - 500 von 554 Bewertungen für Machine Learning: Classification
von Chitrank G
•The course is excellent for beginners.
von Gareth W J
•A good course to teach the key points.
von Hexuan Z
•could be more challengable homework!!
von Vladislav V
•It feels like it lacks certain depth.
von S G
•Course material can be much better
von Farmer
•Exercises are way too easy.
von Aadesh N
•Great course materials
von Xiaojie Z
•Can be more detailed.
von Ragunandan R M
•Good overall course.
von 2K18/SE/035 A K
•content is complete
von Lim W A
•Learnt new things.
von Mehul P
•Nice explanation.
von gaozhipeng
•good introduction
von Alberto B
•Very good course
von Antonio P L
•Fantastic Course
von Anand B
•Great course!
von PRASAD N
•good course.
von Ayswarya S
•best course
von Alberto J L R
•Good Mooc
von Syamsul B
•Great
von VIGNESHKUMAR R
•good
von Serge B
•
good
von IDOWU H A
•B
von Ole H S
•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
•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.
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