Avis et commentaires pour d'étudiants pour Supervised Machine Learning: Regression and Classification par deeplearning.ai
À propos du cours
Meilleurs avis
DC
22 juin 2022
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
24 juin 2022
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.
51 - 66 sur 66 Avis pour Supervised Machine Learning: Regression and Classification
par Süleyman M P
•18 juin 2022
It is very basic and understandable course
par Henrik S
•27 juin 2022
It was everything I wanted it to be!
par Tom J
•25 juin 2022
Top notch. Superlative.
par Pak Y H
•22 juin 2022
Best online course!
par Gabriel R
•20 juin 2022
Very good course :)
par killian p
•22 juin 2022
Very well taught !
par Fernando A
•28 juin 2022
Excellent course!
par Mitchell C
•21 juin 2022
Amazing course!
par Michel M F
•28 juin 2022
Great course!
par Trang Q K
•27 juin 2022
Great course.
par Vuk L
•24 juin 2022
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!
par Sreeraj N R
•26 juin 2022
a great course to understand theory of supervised machine learning. Need lectures for numpy and scikitlearn
par Royston L
•21 juin 2022
I don't understand why the practice lab code for gradient descent and the lab assignment code is different.
par Faizan T
•23 juin 2022
Vectorized implementation in the assignments would have helped
par Mykola S
•24 juin 2022
a bit more complicated tests will be good
par Adnan H M A M
•25 juin 2022
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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