deeplearning.ai의 Supervised Machine Learning: Regression and Classification 학습자 리뷰 및 피드백
강좌 소개
최상위 리뷰
DC
2022년 6월 22일
Excellent course, very logical and well structured. Highly recommended to anyone interested in learning about this topic. Assignments are on the easy side but you learn a lot nonetheless.
R
2022년 6월 24일
absolutely amazing course, coding assignments are designed perfectly and the course helps in understanding the working and the math behind the algorithms which makes it so recommendable.
Supervised Machine Learning: Regression and Classification 의 63개 리뷰 중 51~63
교육 기관: Henrik S
•2022년 6월 27일
It was everything I wanted it to be!
교육 기관: Tom J
•2022년 6월 25일
Top notch. Superlative.
교육 기관: Pak Y H
•2022년 6월 22일
Best online course!
교육 기관: Gabriel R
•2022년 6월 20일
Very good course :)
교육 기관: killian p
•2022년 6월 22일
Very well taught !
교육 기관: Mitchell C
•2022년 6월 21일
Amazing course!
교육 기관: Trang Q K
•2022년 6월 27일
Great course.
교육 기관: Vuk L
•2022년 6월 24일
Andrew Ng surpased himself as far as his teaching skills. I am amazed by quality of his lectures and the way he explains things. However I found that quizes were to too easy. One should just pay attention to what was said during lectures and 100% grade is guaranteed. That's why I'm giving 4.0, although I think 4.5 would be more appropriate. All in all - great first course!
교육 기관: Sreeraj N R
•2022년 6월 26일
a great course to understand theory of supervised machine learning. Need lectures for numpy and scikitlearn
교육 기관: Royston L
•2022년 6월 21일
I don't understand why the practice lab code for gradient descent and the lab assignment code is different.
교육 기관: Faizan T
•2022년 6월 23일
Vectorized implementation in the assignments would have helped
교육 기관: Mykola S
•2022년 6월 24일
a bit more complicated tests will be good
교육 기관: Adnan H M A M
•2022년 6월 25일
In general, I think it was a valuable course to take. I like the way Andrew tried to conveying the ideas intuitively to make sure the students understood the methods behind the learning algorithms. However, I would've loved if there was more in-depth treatment for the Math aspects of the obtained results. Also, the assignments + Optional labs were not as engaging as I hoped. What I mean by that is, it almost required no deep thought from our side to implement the procedures. In other words, there was a lot of skeleton code that makes you "implement" the algorithms with almost no thought (which I don't think is beneficial to the student's learning experience)
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