tldr The course is a great introduction to ML for an audience already comfortable with mathematics and Python. For what it aims to achieve, I think it does a great job. /tldr

T​he mathematics involved in the first course of this specialisation is not that difficult if you already have a solid foundation on calculus. S​ome functions used in the Optional Labs are called for you from already written python scripts (which you have access to, and can download to inspect). The first 3 weeks (and probably the rest of the course) will not teach you fundamentals on Python or mathematics or statistics, and some details regarding the choice of loss function for logistic regression were omitted. Furthermore, libraries such as scikit-learn were used to complement the material, but not explained in depth. (Granted, this course is not about Python libraries.)

A​ll in all this seems like a great introduction to ML for people already comfortable with mathematics and Python.

If you already have the foundations required (Undergrad basic calculus, Python) you can do all 3 weeks in one day fairly easily without distractions.

Excellent content. I'm a math guy so I would have enjoyed some more in-depth theory, but that's what books are for I suppose!

I've been using Python for a long time now so understanding the code was nice and easy.

Thank you for your hard work putting this together!

In general, I think it was a valuable course to take. I like the way Andrew tried to conveying the ideas intuitively to make sure the students understood the methods behind the learning algorithms. However, I would've loved if there was more in-depth treatment for the Math aspects of the obtained results. Also, the assignments + Optional labs were not as engaging as I hoped. What I mean by that is, it almost required no deep thought from our side to implement the procedures. In other words, there was a lot of skeleton code that makes you "implement" the algorithms with almost no thought (which I don't think is beneficial to the student's learning experience)

absolutely amazing course, coding assignments are designed perfectly and the course helps in understanding the working and the math behind the algorithms which makes it so recommendable.

Excellent balance of theory and practice provided by exceptionally well documented and visualized examples and code in Jupyter Notebooks that one can interact with to build intuition.

Really enjoyed the course, had a few questions by the end of it that were resolved quickly in the forums. I would implore others to use them too as they are a great resource.

Andrew Ng is a very good professor, he explains complex concepts in a very simple way and with the help of many visualization and graphing tools. Highly recommended course!

The course is very interesting. I have learnt a deep understanding on machine learning, now I know the difference between regression and classification.

Good, I keept working on these codes and searching for clues in videos. Good structure, reinforcment of some knowledge.

a great course to understand theory of supervised machine learning. Need lectures for numpy and scikitlearn

The quizes are too straight forward and simple. The code exercise too short as well.

Also disappointed that vectorisation is introduced but cost and loss functions are still calculated in for loops.

The best of the best. I am superglad to see the upgraded version of the legacy Machine Learning Course by the super helpful tutor, Andrew Ng, implemented in Python. Very detailed Labs, allowing plenty of practice and intruition. Luckily enough, I was already great at Python and NumPy. I hope the Labs won't be intimidating to a Python beginner.

Overall, this course deserves more than 5 stars. It is second to none, as far as my exposure to Machine Learning is concerned. Thanks Deeplearning.AI and Standford for creating such a fantastic course. I am definitely taking the remaining courses in the specialization😊

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!

It is the Best Course for Supervised Machine Learning!

Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!

Andrew Ng is the best proctor for Machine Learning. The course has been perfectly balanced with thoritical as well as practical aspects. After this course I feel so confident. From ZERO to HERO

I​ started with onld ML course last year, completed successfuly but did not purchase the certificate. As I am more familiar with python than Octave, this new course make thing clearer for me.

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.