学生对 DeepLearning.AI 提供的 Supervised Machine Learning: Regression and Classification 的评价和反馈
课程概述
热门审阅
ED
Apr 13, 2025
Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!
FA
May 24, 2023
The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.
5451 - Supervised Machine Learning: Regression and Classification 的 5475 个评论(共 5,906 个)
创建者 Alter C
•Dec 27, 2023
It is a good basic introductory course, at least in terms of theory. Perhaps those with some experience in python will want more independence in the development of the algorithms. But it really meets expectations.
创建者 Sandhya S
•Jun 1, 2023
Very informative videos and clear instruction. I did find the hints on programming assignments confusing and misleading. I ended up ignoring the hints and accessing previous optional abs for more effective help.
创建者 Dmitrii C
•Apr 23, 2023
A good course to refresh knowledge gained 20+ years ago in the university. The only thing, on which I would advise is to explain normalization a bit more – it is quite difficult to get how to apply normalization.
创建者 SHOBHIT C
•Jul 26, 2025
very beneficial, perfect for beginner's it skips the proof of that hard-core mathematics and directly jumps into the core concepts of ML. But I feel that some more programming can be taught in the above course.
创建者 Chris P
•May 23, 2023
Great course. Just be warned that outside of numpy and matplotlib; functions are defined using mathematical computation and no libraries that have included cost functions, optimizers, or models are referenced.
创建者 Abhishek k
•Sep 21, 2022
For me every single line was important. Everything was great from visuals to complete maths. The only thing I didn't like, this specialization is of 3 parts and all 3 are paid and I can't afford any of them.
创建者 Himanshu S
•Jul 9, 2023
Andrew is brilliant at explaining the fundamental concept, but the lagging thing was practical application, if you could take a real-world problem and code it along with the students it would become great.
创建者 Stefan J
•Jun 29, 2024
Very well done in the substance. The "you don't need to know the detailed math"-statements might appear odd at times for mathematicians/statisticians, but are probably OK for a larger, non-STEM audience.
创建者 Kevin R
•Sep 26, 2022
While I think this course is fantastic I really wish there was some place you cuold ask questions or engage in discussion. If I missed that then my apologies. Overall absolutely worth the time though.
创建者 Tushaam
•Jan 3, 2024
Andrew ng is just fabulous!! however the optional labs must be worked upon since all those complex programming syntax and terms are pretty overwhelming especially if you are beginner to machine learning
创建者 Aniruddha K
•Jan 8, 2023
I learned a lot in this part and would like to continue further but one point that I would like to raise is that it would be better if you can tell us about the in general function that are used in ML
创建者 Wassim B
•May 24, 2024
amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work
创建者 Arpit A
•Apr 30, 2023
Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.
创建者 Kushik S
•Feb 17, 2025
The course was amazing and was very fun to learn with Andrew. The only thing which bothered me was that the audio quality can much more optimised. Though it was the great experience as a beginner.
创建者 Tejas K
•Aug 4, 2023
Content of the course is useful to understand all the important things about linear and logistic regression, like all theoretical concepts. Some codding video's needed to understand coding part.
创建者 Siddharth S
•Oct 4, 2023
I think some additional tutorial sessions explaining python code would have made the course even better . also concepts of vectorized logistic regression could have been covered in more detail.
创建者 Ritik A
•Sep 6, 2022
By far the best course available on internet. It would have been a perfect 5 star if the jupyter notes didnt had functions imported from some other files, rather defined in the same notebook.
创建者 Gabriel B
•Feb 23, 2025
Very accessible, maybe too accessible. Lectures are good, but assignments are not challenging at all. This makes it harder to learn. More of a guided tour of basic methods than anything else.
创建者 Hanlin M
•Aug 9, 2024
Simple and enjoyable learning experience, the only problem was that the content was too scattered without summarizing the lessons, which resulted in me not being able to connect all the dots.
创建者 bjarne h
•Dec 20, 2024
it should be easyer to download videos and programmings files, it seems one has to click on each item. It would be nice to have surgesting to that courses to take after the set of 3 courses
创建者 N4VIN V
•Jul 9, 2025
I actually like the speaker / Instructor in this course. He has a very calm and attentive voice. Just some 2-3 concepts that i wasn't still unable to understand as it feels kinda in rush.
创建者 krishna k
•Sep 25, 2022
Great teaching
Would have been better if arrays and vector operations in python are touched upon.
May be an option course to understand python arrays and vectors which are used in ML
创建者 Gabriel V
•Jul 14, 2023
Very good if you are new to machine learning. Highly recommended if you know nothing about the subject. However I would have like it more if projects were more engaging/challenging.
创建者 Picaña, T V B
•Oct 8, 2025
I learned many things in this course in which I am happy about it. I am just a bit concerned about getting the certificate since I can only get it after finishing the free trial.
创建者 Dinesh D S 5 I B E I
•Apr 1, 2024
A very good start for machine learning journey. The optional labs were the main thing. Moreover we've to focus on self learning in this course. Especially for the libraries used.