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The "Supervised Machine Learning: Regression and Classification, Advanced Learning Algorithms, Unsupervised Learning, Recommenders, Reinforcement Learning" course offers a comprehensive and valuable learning experience. It covers a wide range of machine learning topics, including supervised learning, advanced algorithms, unsupervised learning, recommenders, and reinforcement learning. The course provides a solid foundation in theory and practical applications, making it suitable for both beginners and experienced practitioners.

Excelent course! I highly advice people who wants to get in touch with this topic to give it a try, it has all the tools to go through it even if you know few of programming, however, I would have liked more some challenges in between with real applications, or even additional labs with this real application to try it for ourselves, just my opinion, but in the overall was fun to learn all of this!

I would like to know more on the theory when the actions you can do depend on the state you are in. For instance, in the game of the chess, all moves are not possible all the time depending on whether a piece is on a certain square or if it is pinned... This is the case in many games including the game of Go or even soccer only the player who has the ball is able to pass it to other people.

I think that this course goes very in-depth into how machine learning works. On top of that, he talks about the code and real world applications so transitioning this over to work or school is very easy. The practice labs have simulations and graphs to show exactly what your code is doing. Overall, just a very informative and relevant course.

Very well on introduction the basic concept of the topics, but some of the function are not visible enough for us to understand (such as the update boolean in the assessment of reinforcement learning)

Amazing content, perfectly curated topics with hands-on labs, although Assignments and labs could be more challenging based on certain level students who already have programming backgrounds.

The content was details, explained thoroughly and understandable. But, when it came to implementation, few more labs similar to the structure of previous course could have improved it more.

Recommended resources to help in ML learning would be helpful. For example, Kaggle was mentioned which was so helpful for me. Thank you for such great learning material!

The part on RL is obviously more difficult but gives a good understanding of the foundations and principles.

Overall an other great course taught by Andrew NG!

Lack of hands-on experience in coding (i.e. the implementation of the algorithm). Need more detail explanation and careful guidance throughout the notebook.

Very instructive and interesting. There were some videos were the slides were very cluttered with calculations (e.g., the derivative optional video).

The lab assignments are feel happy in nature. They should force the learner to write more than just the code for hidden layer selection. Thanks!

Great course on understanding key machine learning techniques without getting too deep into the mathematics.

Nice content, the speed of speaking was a bit slow. Fortunately I could watch at 2x the speed.

The course needs more application excersices and no just the theory of the concepts

great course but practice labs weren't challenging nor tested material well

few topics were little complicated , specially reinforcement algorithm

great course and content

The reinforcement learning part can be better.

the practice assignments were very disturbing and took lot of time

Thank you.

I suggest adding more optional labs before practie labs.

I think the exercises could be a bit more challenging

great foundational course with the fundamental math