It is a very well structured course and well focused, the idea of the case study approach is great. The only thing that I disliked was the fact that the jupyter notebooks were explained using the library Turicreate, which has been a great discovery, but it is not as widely used as Scikit-Learn. It would have been nice to have both implementations.

It would be the best staring point for people new to machine learning .The course was very clear and well organized .The assignments and quizzes have given me much deeper understanding of what is being told in the video lectures . The only thing which I felt could get better was using other libraries than graphlab ,libraries which companies use .

It is great to understand overall machine leacning technique. However, one thing which is not good is we should use dato's product, graphlab almost mandotorily. This product is very expensive, so we would be hard time persuading our company to purchase the license. I think it is much better for course student to have special offer from dato

Good explanation and Great Approach to ML using Case study But Sframe and Graphlab installation is a difficult task. Most of the students do not like this just because sframe files did not work at all when you loaded the data set but doing the right approach can make the work easier and just follow graph lab instructions for installation.

Generally, the course provides very helpful machine learning algorithms with hands-on labs. The lecturers explain problems as the beginning stage to machine learning understanding with practical examples. It would be more helpful if there were instructions on the installation of software, such as Jupiter Notebook and Turicreate, in Linux.

A great introductory course to Machine Learning for anyone with experience programming. It's presented as a survey of various Machine Learning techniques and I appreciated seeing many motivating examples for the topics covered. The hands-on examples were accessible, but at the same time gave familiarity with real-world tools like IPython.

In general it is a good introductory course. The lectures are easy to understand and the learning materials, especially the notebooks, are very useful, but it is a pity to know that the last two courses of the specialization were closed. Most of the programming assignments are too easy(just copy-and-paste), which is another disadvantage.

This course gives a good summary of the general machine learning pipeline. However the depth of the course is very low. Also the it uses a commercial python library to implement the models. For these two reasons the course has little value on its own. But this is a good starting point for anyone who wishes to complete the specialization.

Nice course, completed auditing. Last 2 weeks were not quite explanatory, rather they were very rushed i think. Just coding samples, not much learning. Also final Capstone shouldn't have been removed, it reduced the motivation to proceed with the courses.

But what the course did offer, was quite interesting and helpful (I HOPE ;) ... )

Overall nice refresher course. Some of the material was basic.

only downside is that you have to use DATO for the exercises. Different courses have their own requirements, but possibly giving people the option to do this in R or regular python owuld help. Having an optional model with dato where the benefits are shown would be nice.

Very informative and make the machine learning experience much easier for a beginner to all these new concepts. This course is very well set up to help students into the future apply there new knowledge. Only thing is the software was a little difficult to at first get working on my mac but other than that everything was fabulous.

I would've preferred if the used tools were opensource. Also, I felt that in many videos I lost my concentration due to the side comments that had nothing to do with the actual technicalities of the course. Nevertheless, the material presented in this course provides an excellent overview for the foundations of machine learning.

The course was very well taught and the exercises provide a realistic introduction into real-world problems. The only thing that is missing to get to a 5-star rating would be to use standard machine learning libraries (scikit-learn, which is free) instead of GraphLab Create, which requires a paid license to be used commercially.

Great Introduction to Generic Machine Learning Concepts.

One suggestion to the teachers would be to include an optional programming section just to introduce GraphLab to users. There is a lot of API calls which are explained on the go but a high level view of the library with the relevant structuring of APIs would be helpful.

This course is very informative and useful for anyone who have no machine learning background. The case study approach helped a lot in understanding the core of every concept before deploying.

The only drawback was that there was no tutorial on how to install the software which was so tricky and take me ages to install it.

The course is pretty decent but what i really didn't like was it outdated use of software and pretty less efficient mentors.I suggest, to please moderate this course, this course is indeed a good one but need to be supplied with new references and less dependent on particular libraries.Otherwise the course was great.

I think the course met my expectations – it's super high-level and does not at all go over the underlying algorithms involved. I give it 4 stars because I have this feeling like this specialization is an underhanded way to sell the Dato GraphLab Create product. There's a bit of a conflict of interest going on here.

Wass a great introductory course. Definitely recommend for starters. The course was well constructed and presented. The only problem I faced was from the software side. I was having a hard time installing and importing packages, those are not covered in this course. I like the case study approach as an introduction.

The course has everything you need to get an overview of machine learning. It's perfect to understand the purposes and techniques used. However, I'm a bit concerned with practical tasks, since they heavily imply on GraphLab create, and this is a serious disadvantage, since one will barely use it in future.

This course is great if you just started getting into the field of machine learning. (Great if you have no or limited programming background)

Pace is a little bit slow and Programming assignments does not captures algorithms discussed in lecture.( Although it mentions, it never let you implement yourself.)

Very good introduction to machine learning, quickly enables the student to perform regression, classifications, etc. but it would be nice if the course went into a little more detail, the quizzes are very superficial. It would also be beneficial to explore examples of applications across different fields.

I enjoyed this class. It does provide a good over-view of the different machine learning algorithms and their practical applications. My only qualm was that the programming assignments seemed somewhat irrelevant as the underlying structure of the different machine learning algorithms had not been taught.

Nicely covered basic ideas about different areas in ML . Hans-on sessions gave a very good idea to solve ML problems practically. Theory explanations where good.

One suggestion i had is about tool used it would have been cool if course was with scikit learn and pandas, since many companies use the same.

I really enjoyed the overall materials and especially loved the way they split the course into two sections:Theory and Programming.

However, i wish they were using more standard packages such as pandas, or skit-learn instead of graphlab. It was a good class to taste what is going on in the world of ML.

Great introduction to machine learning concepts with nice assignments. It seems there needs to be some cleanup performed so that the lectures and content match up a bit better. Overall a useful and approachable course to motivate the need for additional study in the rest of the "specialization."