The concepts on recommendation systems could have been explain better. Having to use TuriCreate instead of open source libraries is a major drawback. Turicreate is also so high level its hard to understand whats happening under the hood. Everything else is great.

Most of the course was fantastically composed but 15% of it felt a little bit sloppy. I was lucky to be not the pioneer of the course because of the discussions which helped me out quickly in these rare occasions. I would give 4.5 starts if it would be possible.

Great specialization series! The way of covering concepts is very interesting. I find it somewhat difficult to solve the assignment using Pandas and scikit-learn. The assignments should be provided with enough help for solving them using these open source tools.

Really good theoretical introduction but couldn't do some of the tasks because my python skills weren't up to scratch, however the course says you don't need any previous python knowledge, so I disagree with that. However, overall a really good introduction.

Generally speaking, this introduction-level course is great, which gives machine learning beginners some intuition in this field. But it is not enough for finding a job or doing research, I think finishers of this course still need to learn more to advance.

Very good practical approach for an introductory course, however you should have some basic knowledge of programming otherwise it'll take you too much effort and time to complete the tasks assigned. It is also great that Python is being used in the tasks.

The videos are a bit old still the course is very helpful and really enjoyable. The Case Study approach is something that really help me learn things and keep my interest engaged. Thank You prof. Carlos and prof. Emily for making this course a great one.

It was a good introduction. Although, I would say that, being part of a specialization, I would prefer a more brief introduction, because very important points are left for the future and you have to spend too much time in a content tha is incomplete.

The last module was a bit hard for me to understand, but everything else was presented in a very clear way. Great class overall and highly recommended for those with some basic Python skills and a desire to see what machine learning is capable of.

this is an excellent course , for those who really wants to learn machine learning . It has a good lectures and courses and programing assignement . but there one small program is that it uses graghlab create theat works only with 64 bit computer

I would give 4.5 stars if I could. This is a very good introduction to the whole Machine Learning Specialization series given the topics covered, especially with practical cases. However, as a standalone course it lacks depth for a 5 star rating.

Very good material with a lot of real-life example for having a high level intuition on basic machine learning algorithms. However, the assignments are not very challenging. I hope this will change during the next courses of the specialization.

Really great intro to machine learning - I think this class would have been better if the programming assignments had been a bit more difficult, but overall really enjoyed this class and looking forward to continuing with the specialization.

The course was interesting and the instructors seemed to care about the outcome. The issue on this course and this specialization in general is the use of their own private software instead of open source frameworks like pandas and sklearn.

Great course to get started on the ML journey. I had some initial difficulty in setting up the environments and manage the various package dependencies. But once it was set up then the rest of the course was really enjoyable and enriching.

It is a nice course for me to review some data science concept and learn a little about deep learning. The use of Graphlab is a little annoying considering rarely any companies use it but the code used is easy to understand for beginners.

Good introduction to machine learning concepts and I'm looking forward to a deeper dive in later courses. Would have preferred the course use industry standard, open source tools, but GraphLabs/SFrames didn't cause an issue for me at all.

Very well driven by the lecturers. Very good overview. My only concern is the difficulity in setting up the working environemnt for python.: I needed to solver serveral issues not mentioned in the guidelines, which took me several hours.

Thoroughly enjoyed the case-study approach toward learning fundamental machine learning concepts. The abstraction level is a great match for professionals who are beginning to understand key machine learning concepts beyond buzz-words.

Great course! I had to watch some of the videos over and over again for the material to really sink in but after I understood what's happening it was very enlightening. This course will really get you hooked into learning data science!

Very good introduction to machine learning. Concepts are well presented and explained, i would have preferred to use a Open Source library in the assignments, but is not bad to know how to use Graphlab, a very powerful tool, for sure!

Lab work instructions are not well written, it is taking lots of time to understand what we are trying to do. Next thing is we need to know the right answers after certain threshold so that we can understand where we are doing wrong.

Very good approach to this topic. Very good explanation even of complex subjects. It is a general overview and allow the student to have a basic knowledge of all machine learnings sub-topics. Maybe to depended on dato dato framework.

It's a practical approach to different machine learning techniques, it would be great not to depend a lot of DATO, but I think at the other hand that dependency allows to focus in machine learning than in program things with python.

Best way of teaching such peculiar concepts with so ease using the best live examples . Great sessions and great energy of Carlos and Emily made it cool and much more simpler to learn . Had a great learning experience with them .