Very nice course for learning. it would have been great if it had some coding practices also, like to have experience of coding it for beginners. This course much more looks lika a theory class. But anyways It was very very nice having this course

This is an exceptionally good course for beginners in machine learning. It covers all the introductory concepts really well and without losing any mathematical rigor. I would recommend it to anyone who wants to start out with machine learning.

Good or must say way better course than any other to start off with . I have been into Machine Learning from past 7 months but this course really helped me to understand the concepts and algorithms at more root and basic level than any other .

The content is good quality and very well explained, the course should encourage more effort from student to apply what is learned. I found most of practice labs too easy and in many cases zero effort required as everything was done already.

I find it absolutely wonderful. Any beginner can easily understand it.

I think one way to make it better is to add additional lab videos for doing optional lab stuff.

Enjoyed a lot, Andrew Ng you are greatest teacher I have ever met. Thank You.

The only negative mark I have is that I no longer have access to my code. There are many other applications that allow you access after you have taken the course. I don't like that I have to subscribe in order to use the code I'd written. :/

Great Course, It helped me gain a strong foundation and the content given in each of the labs and the practical implementations made the course most productive and interactive. Would've love more practical projects in the end of the course.

Really good course and very nice teacher. However, it's missing real world projects from scratch to practice instead of having already completed python notebooks. Also the quizzes should be more complex. Sometime we only have one question.

The course material is pretty good, actually kind of great! But, what I think the course is missing, at least so far, is more problems that can be solved. I think adding additional problems, maybe with less hand holding would be great!

Very good course. It shows all of the mathematical components of machine learning and how we can utilize these math skills to put together a reliable model while making sure it is accurate to the data and the trends we see are right.

Skit learn library is not teached properly and also the labs doesn't tell how to implement by writing your code own rest is good content is good teacher is good the only problem i had is with the labs and the actual implementation .

The Coursera Machine Learning Specialization offers an excellent foundation, covering essential algorithms and concepts with hands-on coding exercises. Ideal for beginners and intermediate learners looking to build solid ML skills.

The course was very elaborate, and the exercises were illustrative. The community framework needs very much work to be done. Although the mentors were responsive, the sense of ownership of the functional issues was lacking!

Great course.. andrew goes much deep in clearing the concepts about Linear regression and Logistic regression.. even though it says it is a beginners course one should have prior knowledge of what are they getting into..

Exceptional. I do not think any other course can teach machine learning better than this. But the projects were mostly there to explain the concepts. I feel like there were no projects made for the sake of making them.

The course instructor should have made the assignments harder otherwise people can just copy and paste the code provided by the instructor to pass the entire course; this lowers the creditability of the course a lot.

I think it was great introductory course and very nicely taught by Mr. Andrew Ng. Learned practical skills with lab practices. I look forward to completing other 2 courses and get specialization in machine learning.

It is a good basic introductory course, at least in terms of theory. Perhaps those with some experience in python will want more independence in the development of the algorithms. But it really meets expectations.

Very informative videos and clear instruction. I did find the hints on programming assignments confusing and misleading. I ended up ignoring the hints and accessing previous optional abs for more effective help.

A good course to refresh knowledge gained 20+ years ago in the university. The only thing, on which I would advise is to explain normalization a bit more – it is quite difficult to get how to apply normalization.

Great course. Just be warned that outside of numpy and matplotlib; functions are defined using mathematical computation and no libraries that have included cost functions, optimizers, or models are referenced.

For me every single line was important. Everything was great from visuals to complete maths. The only thing I didn't like, this specialization is of 3 parts and all 3 are paid and I can't afford any of them.

Andrew is brilliant at explaining the fundamental concept, but the lagging thing was practical application, if you could take a real-world problem and code it along with the students it would become great.

Very well done in the substance. The "you don't need to know the detailed math"-statements might appear odd at times for mathematicians/statisticians, but are probably OK for a larger, non-STEM audience.