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.

A few things were left out, which albiet would have made the course slightly more complex, could have been added as optional / honors content for those wanting to atleast understand fully what was going on. Especially in week 3.

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.

The course has been very informational, and even though it had some complex topics, it never felt too complicated to understand as all the explanations and examples were really great.

Very good and broad topics. Most lectures were great, but found some lectures hard to follow. Test were fair, labs were great but I had difficulty running on personal environment.

Week 1 was a great amount of content, however, week 2 and 3 were really hard to follow in terms of how everything translates into code. But overall, I enjoy every class.

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!

The coding exercises aka lab assigment is extremely hard, learner should have experience on Python, Numoy, Skitlearn first hand before enrolling this course

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 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

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!

A bit harder than the other two course, would have been great to have more information on the same, possibly more in depth tutorials.

the content and theory are very good in the course, Andrew is an amazing instructor I just wish there were more coding exercises.

the course gave me a broad and deep view of what's available in this discipline, what is upcoming in this discipline as well.

This is an amazing course that covers fundamental unsupervised learning algorithms through real life problems and examples.

The course was good conceptually but it could be better if it some how includes the explaination of code parts more often

All the contents were excellent except reinforcement learning. The videos seems very less and not very understandable.

thanks for the course, I just feel in some subjects we could deep more to the mathematical aspects of subject.

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

It felt a little rushed compared to the previous 2 courses. Still really good, but it doesn't go as deep.

Theory is good lacks a bit of practice they have regular labs but as self learner they are hard to grasp

The labs are not very challenging, maybe some more coding would help to understand more material.