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.

I found the tests hints free pretty hard, I know this is more like a theoretical course, nonetheless I wish we had more real life examples and the questions were more about find a bug. Although like the idea of implementing from 0, tysm.

Good one, I liked the lab and the way it works flawlessly. But I have had this one issue where the first lab did not work, which I think was due to the fact I tried submitting it in the night. Other than that, this course was taken well.

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.

very beneficial, perfect for beginner's it skips the proof of that hard-core mathematics and directly jumps into the core concepts of ML. But I feel that some more programming can be taught in the above course.

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.

While I think this course is fantastic I really wish there was some place you cuold ask questions or engage in discussion. If I missed that then my apologies. Overall absolutely worth the time though.

Andrew ng is just fabulous!! however the optional labs must be worked upon since all those complex programming syntax and terms are pretty overwhelming especially if you are beginner to machine learning

I learned a lot in this part and would like to continue further but one point that I would like to raise is that it would be better if you can tell us about the in general function that are used in ML

amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.