The course was really informative but is a little outdated as it uses Graphlab and SFrames which is available in the older versions of Anaconda and Python. It is also a very tedious task to study this course on windows. I had to install Linux on my system to study this course. I gave it four stars because it cleared the concept of ML through Case Studies.

In general, a nice "into" style course to show the capabilities of different ML solutions. However, trying to be so "cool", "awesome", and "exciting" slash back easily: the first thing I remember from this course is that annoying squeaking giraffe, and not the real content.... Nonetheless, If you need a practical introduction to ML, its worth the effort!

Although is a good for a start, and Graphlab framework is state-of-art software. I would like the same content developed using mainstream opensource frameworks like pandas, scipy, numpy, etc.

One of my interest in this courses was that they used python instead of R (which I don't like too much for it's inconsistencies). Python is faster and more reliable.

though it was a great course i was a little let down when module 5 and 6 were taken off the specialization series. A great course for beginners to understand machine learning as it introduced the aspects of it without getting too involved in math so that we could grasp the basics first. definitely will recommend to those wishing to dip their toes in MI.

Course is good starting point into machine learning specialization, it does not provide a good insights into the concepts as such but provides a good overview of different machine learning concepts and keeping the user interested in the course at the same time. I would recommend pursuing the course if you are starting into the machine learning domain.

I learned good material but it was very confusing getting started with graphlab create. Supposedly there is a switch to Turi Create, which I received an email that stated '..the content in this course has been updated for Turi Create', but none of the content is updated. I still got everything to work and do feel I got what I wanted out of the course.

Great course. Good for a broad overview. If you already know basic concepts like regression, classifiers, etc from a statistics class it might be a little slow. Also, the class is taught using graph lab which is not a free software. If you wanted to try it something else that is free, you could... But you will spend a LOT more time on the assignments.

it was great tho the week 6 quiz was quite difficult. a lot of comments in the discussion forum were helpful for me to complete the quiz but lots of feedback suggested improving the lessons to match the quiz or vice versa.

i think it would also help upon submitting the quiz to display the answers you chose , not jsut whether they were correct or not

Fantastic course! Great teachers and very nice to see real-world applications in action. Would have rated 5 stars if an open source library like scikit-learn were used. Students can still use sklearn, but all the examples are using GraphLab Create, a great library, but you need a very expensive (at least for my budget) license to use it commercially.

Before taking this course, I read users' reviews, I knew that this course is a bit out-dated and to my surprise, it mostly uses the proprietary library, graphlab, turicreate. But those obstacles didn't stop, I vowed that I'd learn the concepts but implement the exercise and other things using open-source packages, like Panas, Scikit-Learn and so on.

Very good introductory course on Machine Learning. Be prepared to dedicate extra time to explore the turicreate API. Overall well packaged quizzes and exercises. I found the explanation of math in some areas (for example recommender systems) somewhat lacking; however there are further courses in this specialization which might cover things in depth.

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.