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.

I would have liked a bit more hands on coding and going a bit more deeper in the math and coding. Having said that, on the following course, having these understandings and intuitions have been very helpful to better understand how neural networks work behind the scene. I do recommend this course.

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.

The course is great and offers a wealth of details, both theoretical and practical. However, as a first course in machine learning, the coding in some labs could be simplified. The labs could have been designed using simpler code, particularly by utilizing NumPy instead of looping over vectors.

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.

It was really insightful, learned about all the theoretical as well as some practical knowledge, Andrew sir's way of teaching is definitely the best way to teach something this challenging, really loved the course, Will surely recommend to someone who's starting out on Machine learning.

This course was very helpful for me to understand the mathematics and intuition behind those machine learning algorithms. However, I believe the assignments could be slightly more challenging and demanding, and it would be great to have a small project as a culmination of the course.

Amazing coverage of Linear and Logistic regression! There is a significant attempt to lower the math bar for this course, which is appreciated and admirable; but then the labs require implementing equations in code, which requires some focus be paid to that portion of the learning.

Lectures and assignments are well very curated, but assignments are far too easy and encourage explicit imperative programming (for loops and mutated state) rather than vectorisation, which goes against the taught material. Very odd - that's the reason for the 4 stars only.

Great course. If you would improve something I had a little hard time completing the test's because the code implementations were different to the ones seen in labs. the name of the varbiales changed and some thing's changed so I think this could be difficult for beginners

It was my first specialization which I completed from Coursera and it was worth my time. The knowledge shared by Andrew Ng is just incredible and I enjoyed the course a lot. Thank you Coursera for making such courses available for us and financial aid was also very useful.

One of the best Courses on the internet on ML, I am thankful to this course for existing. But if I had a single dislike was that, there was not much hands-on practical practice. Its more theoretical. I would have loved for Mr. Andrew NG to teach us code side by side too.

Great course for mathematical fundamentals of Machine Learning. However, I would like the optional labs (that introduce and describe the necessary Python code) to be better explained and walked through. It is hard for me to understand how the passage from concept to code

course is extremely awesome but there is just one problem that if this course can be audited for free, then there must be some option to mark it as completed without issuing any certification, so that it won't be a part of My Learning section as it irritates me somehow.

wonderful course, and this updated version is much better than the 2018 version which I took. The key thing I found is that I needed to know more Python to pass each weeks ending lab before you could go onto the next week lectures, so more Python knowledge is helpful.

Simply magnificent. The course does an excellent job in teaching the fundamentals of regression and classification. However, it would be amazing if the course went into more depth into some of the underlying principles used, such as MLE in logistic linear regression.

Very good, intuitive teaching.

I would have prefered if more teaching/videos were dedicated to the Python implementations and more rigorous explanations of the concepts. However, I'm satisfied with what I've learned as I feel as if I'm now able to apply the skills.

I feel the course is great but the lectures only focused on theoretical aspect,I believe adding few coding solving or error rectification lessons might increase the quality of course as just looking at optional labs and so the coding assignments felt too difficult

Andrew Ng is a great teacher. I took his original Octave-based Machine Learning class when it came out years ago. I've been waiting for the update in Python. I have to say, this class is not as hard or math-intensive as the original, but so far, it's been great.