I learned a lot from this Machine Learning course! It was rather general, but that was what I expected from the first course in the series. In my opinion it was worth the money as the quality was high and it provided an extremely good starting point in this area. I'll definitely be purchasing the next course in the series.

Great course. I really enjoyed going trough all the classes with Emily and Carlos. The case study approach is also very compelling. Loved it and really recommend it to anyone curious about ML.

Some previous experience in Python is required, which I hadn't, so I had a quick Codeacademy python course that really worked well.

Got the best course so far to introduce me to the concepts of Machine Learning. Kudos to the instructors Emily and Carlos for providing a well laid out syllabus with an approach that was grounded on practical concepts and demonstrating hands on with real world examples. Hoping and requesting them to keep up the good work.

This course offers a broad range of examples in ML. Clearly some basic knowledge of linear algebra and other concepts is needed, but I believe it is well structured to help those who're not so strong in math. It really is basic, though, so if you have already some knowledge in ML this will result sometimes a bit slow.

This is a very good introduction to ML. I felt that everything was presented in a very straight forward manner. A little more guidance on installing python and jupyter would be beneficial for those that have not used python packages much.

Overall a great course and I am looking forward to the more in depth courses :)

Great overview and engaging introduction to regression, clustering, classification, and deep machine learning with hands-on ability to see some of these practices in action programming exercises in Python. Good introduction to the more in-depth materials which will be covered in other courses in the specialization.

A good course for a specialisation. I faced Little problem in downloadins of graphlab,turicreate,matpotlib .as it was mention to download only turicreate.........but I had to download all graphlab and matpotlib. But the trainers were just awesome. Explanation of the Topics with examples were really understandable.

This course is very interesting and teaches you the basic concepts and practice applications of machine learning technics. The only drawback is that this course rely heavily on graphlab package which cannot be used in Python 3.7. Took a long time to search for alternatives in sklearn instead to finish assignments.

This is an excellent course... One can tell that a lot of effort went into making this course fun and easy to work with... Almost certainly the most fun to work on course I've taken on Coursera so far... The instructors are very nice, the video lectures are fun and the assignments are easy and fun to work with...

It was greate and complete foundation course on ML which i have taken by University of Washington department. The lectures are very clear and can adopt to the real world problems. i am very much thank full to the faculty for such an wonderfull case study approches given in the entire course.

thank you once again.

Excellent course and highly recommended - covers fundamentals, TF-IDF, cosine. jaccardian similarities, recommender systems (precision/recall, AUC), deep learning via transfer learning (not having to explicitly build a model for the problem).

Exercises could be done in some tool which is common across industry.

Awesome course! Only gives an introduction into the Machine Learning topics but does it well. As a Technical PM in the software industry, this was enough depth for me to understand the basics of machine learning algorithms. Also has good hands-on tutorials with Python to implement the algorithms which is great.

Excellent course!!... It has been the best online course so far. I really enjoyed the Use Case approach, and got really excited with the fact that –although being an introductory course- I got really a good intuition and hands-on experience about use of machine learning for real applications.

Congratulations!!

This course helped me develop a good understanding of complex machine learning concepts.

The tools were easy to use and helped me learn quickly. Unlike other programming classes I've tried in Coursera, I did not have to deal with programming environment related problems. I learnt important python skills also.

The course sets a strong foundation for someone who wish to specialise in the AI and ML space. The course content is easy for a beginner with a very little or no (you gotta believe it) software coding background. The instructor are awesome and help you to go thru the course with ease and not getting bored.

There is so much of flexibility. It is so cool and so interesting... I could complete this complex course so easily with some of the key activities like below:

Exercise videos

taking quiz questions multiple times with no penalty

simple English and explanation of complex information in simple and easy terms

this is course is very good for a beginner who wants to know what is machine learning , why we want this , what is its application .

also you will understand many algorithms used to manipulate data to do very cool applications and you will do this yourself .

they made it very easy to understand , thank you .

It is a very well structured and effective course. I really learned a lot of machine learning techniques that I can use immediately. Both instructors did great job explaining the concepts and algorithms. Very powerful python tools are introduced, and I love them! Definitely worth the money that I paid for!

Emily and Carlos have done a great job in preparing this course. This course is for anyone who doesnt have any background of Machine learning. The hosts have taught the course by implementing a practical approach. I have learnt a lot out of this course and i hope to complete the remainder of the courses.

It is a good introduction to machine learning with cases. It explains all the big concepts in a high level, and uses all the out of box functions of graphlab to implement those ideas. Do not expect to have super detailed understanding of all the algeralisms and step by step how to do it from scratch.

I found it very useful in terms of getting used to python programming, jupyter notebook and machine learning concepts. The case study approach gave me an opportunity to immediately apply the machine learning concepts learnt in the course. The course structure is very well organised to do the same.

Great SURVEY of use cases and methods in machine learning and an opportunity to familiarize yourself with Jupyter notebooks, Python and GraphLab Create. This is an orientation to machine learning; none of the use cases or methods are covered in great depth (that comes in the courses that follow)

This course gives overview of what we are going o learn ahead in machine learning course. Carlos and Emily they both explain stuffs in very detail manner. IN fact it so much fun to learn when you understan thing and specially these cool stuff i hope to see some more courses on this in future. :)

I think this course is a quite cool fundamental Introduction. After finishing the course, you can do real things like building a MPC (Model Prediction Control) system using regression technique and so on. I fully encourage you guys joint this course for a getting started step into the ML field.

Very well presented! The tools are explained and provided in a way for genuine learning and application. The jupyter notebook assignments allow one to jump into the material, while taking detailed notes along the learning 'journey'. I am excited for all the other courses in this specialization!