2020년 6월 14일
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 :)
2016년 10월 15일
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!
교육 기관: Kumar B•
2017년 10월 4일
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.
교육 기관: Neelkanth S M•
2019년 4월 8일
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.
교육 기관: D B•
2018년 6월 13일
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!
교육 기관: Eric A J C•
2021년 8월 5일
The videos were excellent, and the extra material to delve in deeper in the subject were very nice. However, the programming assignments were mostly chunks of ready-made code, so not much is left to the learner.
교육 기관: ANGELICA D C•
2020년 9월 22일
Finalizo siendo muy confuso. El conocimiento de los videos opcionales no se le daba seguimiento, hasta el final en las tareas es cuando se usaba pero ya estaba fuera de contexto y era difícil entender.
교육 기관: Supharerk T•
2016년 7월 6일
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.
교육 기관: nazar p•
2017년 6월 29일
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.
교육 기관: Rohit J•
2016년 5월 12일
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. :/
교육 기관: Ilan S•
2016년 11월 23일
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
교육 기관: Rahul S•
2020년 6월 17일
Too much confusion, I face too much problem with this course. much confusion if you use different packages like sklearn.
교육 기관: Lawrence G•
2016년 5월 19일
The course content seemed to be rushed out, as a result, the quality is not as good as the first two.
교육 기관: Tu L•
2018년 6월 27일
Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.
교육 기관: Mounir•
2016년 6월 19일
Exercises for Scikit-learn users were not organised.
Course took too long to start
교육 기관: Pier L L•
2017년 3월 26일
Nice course but I would have expected more techniques (SVM for instance)
교육 기관: Dmitri B•
2017년 6월 6일
Theory Quizes are good, but programming assignment not so good for me.
교육 기관: Ashish C•
2019년 3월 31일
more topics like deep learning, neural networks need to be introduced
교육 기관: Matt T•
2016년 4월 12일
Good, but overemphasizes niche software product (graphlab).
교육 기관: Virgil P•
2018년 2월 18일
The exercises/assignments are far too simple
교육 기관: 陈弘毅•
2018년 2월 3일
교육 기관: Deleted A•
2020년 8월 13일
교육 기관: Omkar v D•
2018년 8월 14일
교육 기관: Rohan G L•
2020년 8월 29일
I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.
You will learn about Data Science and Machine Learning, but not much about Python.
The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.
There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.
I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.
교육 기관: Amit K•
2018년 1월 20일
The video content is awesome. Important concepts are being clarified in a very simple manner. However the evaluation method really sucks. First, there is too much spoon feeding in the programming assignments, which was not the case in earlier courses in the same specialisation. Secondly, in a few assignments, the answer to the quiz questions are sensitive to the platform we are using (like PC vs AWS instance). This was really frustrating given that the issue is known for a long time and has not been fixed yet. At the very least, there should be a warning on the quiz page itself.
교육 기관: Yaron K•
2016년 9월 30일
The assignments are well thought out and explain the algorithms step-by-step. The subtitles/transcripts are a disappointment :( . Full of mistakes. Sometimes to the point of being useless or even worse - saying the exact of opposite of what the lecturer says. Since the lecturer sometimes is unclear - this is problematic. As usual - Graphlab Create sometimes crashes, however there are explanations how to run the assignments using Scikit-Learn.
교육 기관: Matthew B•
2016년 4월 4일
The content seems rather thinner than that of earlier courses in the specialization, and seems to get more so as the course progresses. (Week 6 is entirely spent on Precision and Recall, with only about 30 min of lecture.) It feels like there was a rush to get the course out and that corners may have been cut at the end.
And as others have mentioned, several very important classification topics are conspicuously missing.