Having done some other machine learning MOOCS , this course seemed rather basic to me and did not enjoy too much using non open-source software for the programming assignments. The material is nice, In this sense, I would have expected to 'default' to sci-kit learn and offer using graphlab create as optional.

While it was good for learning concepts I had real trouble with graphlab. Installation of graphlab never worked on my machine. I had to install VM just for being able to use graphlab. I really wish they had opted for more open source, free options or at least used ince such library along with graphlab.

There are big problems in this course, like the installation process should be given in a more specific and vivid way so that I would not have spent three days on it being a windows user...(update: still can't access jupyter notebook after trying installing ubuntu, vmware workstation, filezilla).

Too much dependency on Graphlab package is bad. If we are learning the concept, we should reduce the size of the sample data. We should be using generic open packages, so that our learning can be easily demonstrated anywhere (especially interviews), and not dependent on graphlab.

The case study approach is a great idea.

But I wish the instructors were more candid about the tools that were in use. It seems dodgy that the instructor is a CEO of a commercial tool vendor and is "encouraging" students to use it.

The quizzes in the course were extremely shallow.

I believe these packages are out of date and the application side is not helpful.

The information on the theoretical side of things was extremely helpful to help build up my machine learning knowledge, but overall I don't feel like I'm taking away much from this course.

The overall quality of the course is good, but in my opinion the level is quite low and there is less content then I expected. The assignments are more or less copy-paste or very repetitive. The 5-8 hour work per week are a joke, I never needed more than 2.5h per week.

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

It is more a course in using the tools designed by the teachers than machine learning. It might do something for a less experienced user in programming, but I didn't find it much use. The overview of Machine Learning tasks isn't bad.

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

The Assignments and Quiz questions are hard to read and comprehend.

Although individually the course presentations are ok, overall this course isn't a very relevant or coherent introduction to Machine Learning.

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

The course uses proprietary tools for machine learning and data manipulation, making it effectively useless! However, the material on describing the machine learning algorithms were excellent!

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

If someone is looking for ML foundations and what is ML, they can choose this course. This is very basic course and i feel should be excluded from the ML specialization.

One would learn a thing or two, but the course is very sparse compared to other machine learning courses, and I didn't feel that it was worth the time and the cost.

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.

This is a good idea to use the case study approach. However, there are issues with files and program setup that stopped me from continuing with the course.

The framework is ok, but it would be more insight on the functions would be much more amplifying the learning process.

Good working examples, though!

I thought that indepth will be taught and enrolled for this course, but unfortunately its only basics. I wasted my enrollement

It started nice but there are some basic aspects, like installing Turi Create, neglected. I had to withdraw from the course.

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.