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.

Every concept was explained brilliantly by Andrew Sir. Though the lab sessions code is much simpler so I feel the code level should be increased. Overall the best course to start your ML journey with :)

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.

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.

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.

Very good learning experience. Bit difficult than the previous course on supervised learning, but concepts can be understood if we listen and understand carefully.

very good course but it needs lab practice , it looks like we just run the code , aand if we want to write by our self it is hard . but thank you so much

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.

Explains the working behind algorithms well . But you need to spend time actually building some projects with this knowledge on your own

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

If the lab materials and implementations were described It would have been the best course on Machine Learning Basics

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