The present course content is relatively relaxed, i.e. whatever is covered in lecture videos is directly asked in the assignments and labs, which makes it a bit boring. A humble suggestion is to make the course content challenging and include brainstorming assignments and some questions requiring deep thinking and concise concept clarity. But honestly, a lot of effort has been put in, be it interactive simulations in optional labs or lucid explanations.

The course in general has been great! I think it covers perfectly the main topics and skills of supervised machine learning. Nevertheless, I find the practical exercises maybe to easy to accomplish even without properly understand what your are doing. Maybe as a final project would be interesting to propose a less guided exercise. But congratulations! It is a great course, really useful to have a taste on this wonderful world that is Machine Learning!

NOT EVERYBODY CLEARLY UNDERSTAND WHAT THE CODE SAYS. THERE IS NO VERBAL EXPLANATION OF CODES, IT WOULD BE BETTER IF VERBAL EXPLANATION OF CODES WAS AVAILABLE. BUT NONETHELESS I THINK THIS IS THE BEST COURSE I HAVE TAKEN ON SUPERVISED LEARNING. EXPLANATION OF THINGS ARE REALLY TAKEN INTO CONSIDERATION AND BEST POSSIBLE EXPLANATION IS PROVIDED. I HOPE THEY ALSO LOOK DEEPER INTO SUPERVSIED LEARNING AND VERBAL EXPLANATION OF CODES.

The course design is so excellent and I really enjoyed the course. However, I gave it 4 stars because I would really appreciate it if there are some information about the real-life use of this. I am the kind of student looking for some real-life implementation to understand the course more efficiently. Overall, thank you so much to the team and Teacher Andrew Ng for the kind guidance. Best of luck.

This gave me a really good overview of how to solve Linear Regression and Classification problems. I learned a lot about prominent Python-based machine learning tool-kits, applications and libraries. It comes with some extremely valuable insights from one of the AI field's experts. Being an introduction, this course does, from time to time, skip the specifics. I thoroughly enjoyed it.

The course is great regarding content and explanations. On the other hand, it could have more practice tasks that one should do on their own to better understand the topic and grasp knowledge in the field. In the last practice task in the section for user's input, there is a suggestion to use inefficient code without vectorization which is in ML crucial as professor Ng mentioned.

The content covered is interesting and explained thoroughly and in a very clear fashion however I find the practice labs a bit underwhelming, they have too much assistance and also the pre-existing guiding code can lead to avoiding vectorized code which supports slower algorithms, I feel like this course could have better practice so as to be easier to apply in other applications

Andrew Ng surpased himself as far as his teaching skills. I am amazed by quality of his lectures and the way he explains things. However I found that quizes were to too easy. One should just pay attention to what was said during lectures and 100% grade is guaranteed. That's why I'm giving 4.0, although I think 4.5 would be more appropriate. All in all - great first course!

It was a great course for introduction to Machine Learning. I enjoyed the course very much. One thing I would like to add is there should be an exercise to write full code, because that would enable us to understand better what variables to take and what algorithm we should follow without any existing template and all. Otherwise I liked this course very much

The content was good, but I think the quizzes and assignments weren't designed focusing the development of intuition and a deeper understanding of the content. The same goes for the optional labs. I would say to take some inspiration from the courses offered by Imperial college. But overall the topics were covered in depth and effectively by the instructor.

Great Intro to ML. I did not find it challenging enough or offering extra info that we can study on our own (like generalised linear models). It also doesn't mention that there are other parameter optimisation algorithms other than gradient descent. Overall a very beginner friendly course, but left me wanting for more, which isn't exactly bad I guess ;)

I appreciate the example-driven approach toward these complex topics. One thing I feel missing is the practical sessions on the coding part, although the coding part is provided in the optional labs times it feels a bit confusing and requires some further explanation on it. An overall, very useful course for those who are looking for the fundamentals .

The code need to be explained because there are many functions student doesn't know. I searched and knew these functions but sometimes I couldn't understand why we used this fun while there is another one can do the same. and many things wasn't clear to me in the optional labs.

But the videos were excellent and I recommened the course to my friends.

The course was very helpfull and the instructor made the course very easy to understand ,I wish i could thank him in person .But, the challenge was in the jupyter labs it was hard to understand the skills in visualizing the data given and the codes was tricky hope to see more videos of coding in the future.

Thank you coursera.

Thank you sir\Andrew.

The content is very-well explained. I would have liked the practice labs to be a little more difficult. The labs fill up almost all of the code and leave very little to be done by the student, so the labs were not sufficient for me to get a good grasp of the coding skills required to INDEPENDENTLY write code for my own ML projects if any.

Overall good starter courser, but have been good have a little more videos on using the scikit-learn library. Also an exercise is needed where one implements the entire regression solution from identifying variables, creating cost function and the gradient descent. Doing everything from ground up, even if it is for a small training set.

The course was very good. Well-structured and the concepts were made simple enough to understand. The only drawback was the minimal use of libraries such as Sci-kit learn in practice labs and assessments. However, will definitely recommend to people who are interested in understanding core concepts behind Regression and Classification

A very good course to understand Regression and Classification. However its just about the introduction of it. Would have been great if the talk on Mathematics of Regression possibly from R-squared, SSR and such things could also have been there. Same goes for Logisitic regression such as Accuracy, Precision..etc. Good for beginners.

Very good overview, although as a mathematician I found the lack of rigour in the analysis 'disturbing'. It's a bit frightening to think of hordes of people (or, worse, machines themselves...) using tools like this to reach potentially incorrect conclusions without a clear appreciation of the limits of the techniques they are using.

I really like this course. Though for more in depth learning how to apply logistic and linear regression i would recommend Andrew NG older course also still available on coursera. It goes more in depth of how to evaluate a model when implementen and comparing them. Anyway thanks to all the contributors to making this available.

Consolidates all of the essential beginner information for types of regression and gradient descent. Worth it for busier professionals. Wish there were more hands-on "throw you into the pool and let you swim" types of labs, everything is very hand-held. That is what other resources and personal projects are for, it seems.

Was a great course and the visual representations really helped a lot in understanding key concepts, my only suggestion would be a little more depth in coding know how and video explanation of it than just notebooks to read from. Overall a great course to learn in-depth about regression and classification from ground up.

This first part of the course is probably suits a freshman student in engineering who has limited knowledge of numerical methods or computer programming. While I hold great regard for Mr. Ng, I find the pace of the course to be somewhat sluggish, and the depth of the material covered to be modest.

Overall, the course was remarkably insightful and presented in a way that made it easy to follow. In the future, I think it would be beneficial for the lessons to delve deeper into the programming aspects of machine learning, as this area demanded a lot more independent research and self-learning.

This is a really really great course which provides the fundamentals of Supervised Machine Learning and also math concepts used in Regression and Classification. Ones who don't have basic math knowledge and basic python programming knowledge will face some difficulites in this course I guess.