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.

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

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!

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

Pretty great. I kind of wish the assignments were a little more challenging but I realize that it's a beginner level course too.

The last week is less boring, it was hard for me to grab to concepts of the last week, it seems everything magically works!

The course is very informative but assignments solutions are provided. There is a lack of challenging learners to do more.

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.

Really good course, maybe some more project-like assignments would be beneficial to assimilate the concepts more deeply

Concepts are well explained, but the last Practice Lab had confusing problems that were not well-explained.

Amazing Course!! I feel that the Random Forest and XGBoost section could be a little more elaborated.

I wished they added neural networks after all the ML algorithms but agains the teaching was amazing

I found the lab practice a bit simple and technical on one side but not intresting on the other.

This course is really interesting and the lecturer explain each topic very neatly and slowly.

It should have more practices. There's a lot of theory, but just a little practice.

Excellent presentation that provides insights into practical problem-solving