I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
By Dany J•
Good content, but could definitely benefit from a more fleshed out problem to solve. The content beg for a larger concrete coding exercice project.
By Hugo J•
It's easy and more simple than the others in specialization. Can be more deeper into ML project organization management. It's ok, could be better!
By Jordon B•
This course did not contain programming assignments, only quizzes, and was thus considerably less useful, even though the knowledge was important.
Quite some questions are confusing and some are not correct itself. and this course is more concept based, didn't actually get to program a lot.
By Giacomo A•
Contains some useful tips, but they are a bit too diluted - I feel like it could have lasted much less and still conveyed the same information.
By Yancey S•
This course provides some interesting insights into how to approach machine learning projects, but feels a little light on substance at times.
By Even G•
Great content. Some strange audio that I think should've been cut (especially in week 2). I suspect the week 2 quiz is a little buggy as well.
By Mayur S•
The course material can be clubbed with existing courses. It would have been much more meaningful with some examples and hands-on assignments
By Rindra R•
Covered important topics and real-world project considerations. However, the content and assignments are too short to make it a full course.
By Daniel K•
This time it was not that well-structured than the previous courses. I thought we would learn how to structure step by step an ML project.
By José G•
Lots of information, few knowledge
Change name to "Struc. Deep Learning Projects", all other forms of ML not considered, specially for P2.
By Eric K•
Too much similar material to the prior course, and only two simple quizzes, no hands-on programming assignments like in earlier courses.
By Eric M•
A fundamentally very good course with a few technical gltiches that can be easily corrected and some confusing elements to be clarified.
By Bongsang K•
I think this lecture is important for every research scientist. However, there was no programming examples so I was confused sometimes.
By Michael L•
No programming assignments or labs, so too much theory, and too little chance to put same into practice. Not a good value for my money.
By Max S•
Still good but getting much sloppier. Bad editing of the videos, some exercises plain wrong and staff not reacting to forum posts, etc.
By Lars L•
Course materials need some cleanup. Were a number of audio blips, in the video. Material was good but just didn't seem as polished.
By Nitin S•
Decent learning. Though quite some stuff, I felt as repetitive and obvious.
I wish there was some programming exposure as well here
By Taavi K•
Too short on its own (took half a day to go through the whole thing), could have been combined with Course 2 of the specialization.
By Raghu t D•
this session was good it would be more better if they provided the code of them..so that we could be abke to learn more from them
By Denys G•
Felt a bit rushed, each video was full of good tips but personally I think each video should have been a jupyternotebook instead.
By Massimo A•
More theoretical than the other courses in the specialisation but still very high quality.
Short but with a lot of information.
By David P•
Not nearly as good as the first two courses. These two weeks should probably be added into the second course at some point...
By Oliver O•
Would like more applied discussion and for it to be Longer. In particular I would like to see a discussion on class imbalance.