The course is good but once you cancel the subscription, you lose access to the codes. I think that should be change.

Too much theory and very few practice.

I have completed this course in full and as a result, I am highly satisfued with how Professor Andrew Ng explains the materials. Thank you for this! However, I cannot understand, why after completing the course a part of studying materials are not accessible, even though I paid a sufficient price for the course. These unaccessible materials include Python programs which were used as a practice. Frankly, I find it unfair, since this practice would be extremely important to revise the materials while improving my skills in Machine Learning in the future. Moreover, a part of the montly fee was paid also for the practice materials. I may agree that these Python programs can be private, however,there should be ways to overcome this issue. Without the possibility to revise the code it will be much harder to create our own applications and programs.

Unable to Open the labs and submit the lab assignments

Excellent course, it really lays the groundwork for understanding the concepts and some of the math behind it, and provides an opportunity to play with the python code in labs. This is a step up from "AI for Everybody", and a good prep for the Deep Learning Specialization. I'm a data analyst with some coding experience, prior coursework in calculus & linear algebra & basic statistics, and found this a great supplement as I'm also working through the Deep Learning Specialization.

Fantastic introduction to Machine Learning. The labs have been updated with widgets. You can add data points, change the polynomial order and many other changes that makes this a great way to understand how the different components of machine learning are done. Highly recommend.

This is a great Machine Learning course for the first-time learners offered by the best in the field. IMHO, the focus of course is on learning the underlying theories of machine learning rather than short-circuiting the basic concepts to the helpers libraries developed in Python.

Brilliant course! I really enjoy the journey and cannot wait to start the second course. It's such a great thing to have a course like this which is made with great endeavor. And spending time and thoughts on it is even more amazing. I am so lucky to encounter this course!

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

Perfect balance of application and theory, and wise choices in ramping up the complexity gradually. Discussion boards are very helpful, feels very much like personalized learning. Thank you!

Excellent course, very logical and well structured. Highly recommended to anyone interested in learning about this topic. Assignments are on the easy side but you learn a lot nonetheless.

This was a great course to understand all the math and logic that goes behind some of the most commonly used ML algorithms. Interesting and a great start to the specialization.

Good for beginners. If you have taken the previous online course 'Machine Learning' taught by Prof. Andrew Ng, you may find this course much easier.

One of the best ML courses so far. The Course is well designed and very well presented by Andrew NG. I highly recommend it.

Precise explanation of the fundamentals of Machine learning techniques, using mathematical examples and python.

Very friendly for beginners, a good refresher if you already had the knowledge of machine learning.

- Amazing instructor

- Very clear and easy to understand examples

Great Great Great Course. Thank you for this amazing course

ilove how simple and rich this course is

Very clear and intuitive explanation with a great instructor, though the contents are a little too easy, especially for people with a STEM background. More exercise could be set with less guidance (currently it's like writing ten lines of codes for every week of learning). Also, it would be nice if there could be an exercise dedicated to the use of packages like scikit-learn in depth, since that is what most people will end up using the most.

A very good introduction to Machine Learning. I would prefer some more math since this gives me more confidence in understanding, but the course is aimed to a wide audience so that's acceptable. I accompanied the course with Andrew Ng's notes on machine learning.

Andrew sir's teaching made the course interesting and exciting. However, the course was too easy and some more mathematically oriented discussions could have been done.

very good course

The focus of the whole course is on gradient descent. I guess it is needed for some other algorithms but here we could have just found the derivative. If I had no background in math and statistics I would give up ML seeing this.