It's a really good course and it gives a basic intoduction to ML and its fundamentals.

The course just need to be updated, many things are of older versions & it creates a lot of dificulties in completing the course.

Great hands-on and practical introduction to graphlab and python that is pretty useful in the later courses. I great way of starting a specialization with an overview and small samples of what we are going to learn.

Provide high level overview of main Machine learning concepts. With real life cases, it makes it easier to understand the concept.

The course do not include specific technical but rather a high level introduction.

Pretty good introduction. Enthusiastic teachers give you a good overview with a fair sprinkling of details - enough to get wet your appetite and the language to explore the concepts outside of the course material.

It is giving me inspiration and clarity what we can do if we know machine learning , its clearing clouds to see the sky and letting me to reach it . Thankful to the instructors and to the coursera. love open stuff

Very simple black box approach to ML. Pretty much anyone can start and play around with ML following this course.

I would have like to get a bit more into details but this is great introduction course for anyone.

While the black box approach makes it easy to understand and grasp at a use case level, I missed some of the intuition associated with how these algos get the work done. Overall, a good beginner course to take!

It has made a lot of fun, only what I didn't like, that in this course GraphLab is used. We do not use it in my company, so I would prefer to use Open Source Software packages and not one what has to be bought.

This course is awesome, and it fully integrates with actual needs and theories. However, I feel that the theory is somewhat lacking. It is possible that the teacher will talk more about the rest of the course.

love the approach, although the total lack of support (on the forums) is a bit discouraging. also, there are some errors that make it hard to understand the last week's material, but other than that, it's ok.

IT is a good course to start your machine learning journey. It could have been better with more popular libraries like sklearn and pandas. But the course material is very understandable and nicely delivered.

Une très bonne introduction par la pratique aux différentes notions et concepts du Machine Learning, avec assez d'éléments concrets pour pouvoir commencer à mobiliser ces théories dans un contexte pratique.

+ Excellent video lectures.

+ Good overview of the field.

+ Nice working examples with good instruction video's.

-- No help with the practical assignments although the Python examples given are not flawless.

Four years ago, this course deserves a five-star review. But as the turicreate has changed a lot, some functions cannot be used and some error appears in the hand-on part. But, it is still pretty good.

The Course was very neatly presented, although we used lots of predefined functions to work around Machine Learning Algorithms it was good to know about the concepts that was thought extremely well.

I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.

The course was very informative but I face a lot of problems in installing Graphlab and Turicreate. I request the Mentors please use the Pandas data frame in place of SFrame. The mentors are cool.

Good for a introductory course if someone is getting started with machine learning, but as part of an specialization i think is useless (for people who are planning to take all the specialization).

Graphlab is used during the class, where as in assignments, turicreate is used. This causes slight variation in the results between the two. This may cause loss of points in the assignment.

Course contents are good - however the forums are not active and there is no follow up from faculty to update the course specialization following the change in course structure (eg. no capstone)

This course is very good. The only problem is that instead of using open source packages like scikit-learn they decided to based it on proprietary GraphLab (which is free only for academic use).

Good course, a tiles a litte coursory, but a decent introduction to the concepts and vocabulary of machine learning. Something like this should be required for anyone who works with data today.

Decent intro, though it would be a lot more useful if the professor didn't use his Software and instead thought us implementations using python/R which are used in most commercial applciations

It gives you a fair insight to the world of machine learning, without getting into much technical detail. I guess this information is saved for the next courses of the Machine Learning group.

it would be really great if you will teach the provided note book practice examples

and deep learning is a bit harder and faster

instead graphlab if you use sklearn module it would be amazing