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.

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.

Content of the course is useful to understand all the important things about linear and logistic regression, like all theoretical concepts. Some codding video's needed to understand coding part.

I think some additional tutorial sessions explaining python code would have made the course even better . also concepts of vectorized logistic regression could have been covered in more detail.

By far the best course available on internet. It would have been a perfect 5 star if the jupyter notes didnt had functions imported from some other files, rather defined in the same notebook.

Simple and enjoyable learning experience, the only problem was that the content was too scattered without summarizing the lessons, which resulted in me not being able to connect all the dots.

Great teaching

Would have been better if arrays and vector operations in python are touched upon.

May be an option course to understand python arrays and vectors which are used in ML

Very good if you are new to machine learning. Highly recommended if you know nothing about the subject. However I would have like it more if projects were more engaging/challenging.

A very good start for machine learning journey. The optional labs were the main thing. Moreover we've to focus on self learning in this course. Especially for the libraries used.