DG
Apr 14, 2023
Extremely educational with great examples. Helpful to know Python beforehand or the syntax will become a time sync, and understanding the mathematics as going through the class makes it a decent pace.
SL
Aug 27, 2022
After copleting the course I found all conceptual knowlegde for visualising and implementing the algorithm in my work. Before this course I was not using the full potential of the advanced algorithm
By Tiddo L
•Oct 1, 2022
Good introductionary course to advanced learning algorithms.
Main point of feedback: the course did not address in anyway how to actually desigining neural networks, i.e choosing layer size, number of layers, etc. I thought this was a bit odd, since this is a rather fundamental part of NNs. Now that I've finished the course, I'm still not able to build my own NNs from scratch, since I don't know how to choose my layers. I hope further courses will address this, but I think this should've been addressed in this course already.
By Ewa K
•Oct 22, 2023
I am missing handouts from the course and also access to the labs upon completion of the course. It was great that practice labs were offering a lot of help for the student, but I am afraid that too much material was given and the assignment was only about typing the given equation. It leaves me with the feeling that it would be difficult to apply the knowledge from the course to the real word problem, especially that I do not have any code available after the course is finished...
By kiên l
•Feb 21, 2024
Excellent explanation of the concepts by Andrew Ng. However, like other reviewers, I find the last week a little bit rushed and, as compared to the first course of the specialization, this course feels a little...lacking, not in the sense of the information being taught but how the information is being presented (eg. the effort put into making quizzes and labs is subpar ). note: subpar of best is still good so I'd still recommend this one to anyone.
By Nima J
•Nov 16, 2022
It was a very good and interesting course. I learned a lot about machine learning algorithms.
Compared to the first course "Supervised Machine Learning: Regression and Classification" there were a few things missing:
1- Practical exercises
2- Quizzes during the videos
Although you can learn the theoretical content very well in this course, in my opinion there is a lack of opportunities to practically apply and practice the knowledge you have learned.
By Vikas S
•Mar 18, 2024
It is a great course. Only one issue is that the lab assignments don't require much effort (are kind of feel happy problems) which is not good for learning. I didn't bother to check the details of assignments as that was not necessary for writing the code and pass. The assignments should test all the stages of making a ML project, right from data collection, feature engineering, training, validation, etc. Thanks!
By Adnan H M
•Jul 19, 2022
Explanation: 5 starts Assignments: 2.5 or 3 stars
Thus, overall 4 stars. Andrew did an excellent job in explaining the concepts. However, the assignments, in my opinion, were
too easy (almost just running the cells or typing what was shown in lecture videos). I believe challenging
assignments are an important aspect of any course which this course lacks (unfortunately).
By Jayneel S
•Aug 4, 2022
The course material and instructor were very good. I just have one complaint for this course... The quizzes are too easy but sure they capture whether you have paid attention in lectures or not, so that is fine. Also a suggestion - If we could be provided with the lecture ppts it would be really helpful revising.
By Vedant R
•Jun 8, 2023
One of the best mahine learning courses but some of the parts were boring in the middle like week 3 lectures and assignments where you just had theory classes. Some of the parts were very great like all the Decision Tree classes were too good i will never forget how decision tree works now.
Thank you Sir Andrew
By Abderrahim B
•Mar 5, 2023
Some of the topics were not explained clearly and found them quite complicated. Topics:
1. XGBoost
2. Bias and Variance
Also, I did not understand why most of assignments were about writing codes for functions that are already implemented in open source libraries and packages!
By Hoormazd Z
•Mar 22, 2023
Great course. Really easy to follow and it's a good starting point for learning about ML, assuming you already know linear and logic regression. I wish there were more programming assignments and more lectures on TensorFlow though.
By Arshdeep K
•Mar 19, 2024
I wish there was more on the practical use and coding part. And the labs could be a bit more explained. Apart from all that, its an amazing course and it has helped me understand many concepts clearly from the ground up.
By zia u r
•Dec 17, 2022
Truly peaking, I used hints very often because I was not familiar with the Phyton. A optional week as a zero week should be introduce to teach basic python for the students that have no previous interaction with python.
By Giovanni
•Dec 29, 2023
I liked but maybe the level is too basic. Anyway for those who have never seen machine learning, it is a beautiful gentle introduction to all the main concepts explained in a super clear and simple manner.
By Brian R
•Aug 14, 2022
Course material is good and flows well, but are ANNs and decision trees the only advanced algos? Loved the parts on model bias/variance determination and how to fix the model based on the determination.
By Anirban H
•Jul 20, 2022
Beginner level course, explained simple concepts on neural network specifically Multi-Layer Perceptron & Decision Tree, nothing advanced topics covered. But the explanation is very very good.
By Rohan K
•May 13, 2024
It's a great course, really phenomenal but it lacks a little on the implementation side. It would be better if it had 1 or 2 medium-level hands-on projects included in its structure.
By Prejith S
•Jan 27, 2023
I felt the lab assignment in the Decision trees section was a little too fast to comprehend. Otherwise, it was an excellent course with just the necessary theory and intuition.
By Caio A
•May 3, 2024
Good to review some concepts if you have an Intermediate level of knowledge and excelent if you're new to the area. Still very light on mathematics, but the course is excelent
By Ruedi G
•Aug 28, 2022
Very good didactical approach. The labs are straight-forward but test programming skills more than AI expertise. Editing and error checking in the notebooks is poor.
By Avdhoot J
•Apr 9, 2024
It would be much better if you link a relevant applied AI course with this package. The course is more of theory, than practical application.
By Zach S
•Feb 15, 2023
Pretty great. I kind of wish the assignments were a little more challenging but I realize that it's a beginner level course too.
By Gaurav G
•Nov 23, 2023
The last week is less boring, it was hard for me to grab to concepts of the last week, it seems everything magically works!
By Céléstin N
•Sep 8, 2022
The course is very informative but assignments solutions are provided. There is a lack of challenging learners to do more.
By Younas K
•Feb 4, 2024
This one is a little pacy compared to the first one. Maybe the explanation for the math is not as clear as the first one.
By Alessandro G
•Apr 14, 2024
Really good course, maybe some more project-like assignments would be beneficial to assimilate the concepts more deeply