The case study approach for explaining machine learning concepts is commendable. This kind of approach will not only help in cementing the concepts but helps in making decisions when it comes to real-life applications.

These instructors are among the very best I have encountered as a veteran of dozens of MOOCs. Their expertise in the subject matter, presentation and pleasant manner made this a highly pleasurable learning experience.

REAL-LIFE artificial intelligence applications. The examples were so good and real match to the reality, so in this course, I wasn't bored by theoretical information but I have seen its benefits with the code I write.

Great course covering the key models, concept and applications in machine learning. Instructors showed good pedagogy, teaching complicated concepts in ways easily understood. Requires some basic knowledge of Python.

For me this is the best course for Machine Learning Foundations that I watch. It was challenging for me because I did the assigment with R packages. I hope on the future for doing other courses for the specialization.

Excellent course content with emphasis on real-life applications

Great teaching tools and I particularly love the teaching style of Carlos and Emily. Going on with this specialization till the very end.

Great work guys!

It was a really nice way of presenting ML concepts using Case Studies. Giving students an idea of deployment right from the start helps in thinking of an architecture of the system for any project that comes forward.

Love the way the subject is introduced. The course increased my interest for machine learning and also made me understand the power of machine learning first hand. Thank you, Prof Carlos , Prof Emily and entire team.

Un curso muy bien explicado, fácil de entender y unos profesores que consiguen mantener la atención y absorberte en el tema.

Lo recomiendo 100% para iniciarse en los modelos y entender los algoritmos simples de ML.

Loved the case study approach and how it relates to real world problems. Utilizing graphlab also helped abstract away a lot of the details, but I look forward to diving deeper with the rest of the specializations!

Excelent material and instructors. There is at least one of the assignments that needs reviewing because probably the data has changed since the first time the course was offered but the autograder wasn't updated.

This stream along with Andrew NGs is the best ML course available in Coursera. The lectures, especially from Emily's are one of the best. It is perfect for both experienced and newbies. Thanks, Emily and Carlos.

Awesome course ....Both Professors are very intelligent and teaching perfectly....Step by step explanation and also never feel bore because presentation styles are also very best. Thanks professors and Coursera.

The case study approach makes it fun to learn machine learning. The introduction to various topics through specific examples increases curiosity and sets the tone for the following courses in the specialization.

Thoroughly enjoyed this course! Really loved the case study approach of teaching. The instructors are excellent as well, throughout the course it felt like I was hanging out with my friends building cool stuff!

I think the course is really COOL :) I know that it's really hard to cover so many topics, but I would have been curious about the area of reinforcement learning too. Perhaps mentioning MDPs and related models.

Thank you very much for providing us this cool and exciting course. Thank you, Emily and Carlos. It opens a door for me and I've really enjoyed ML so far. Hope one day I could be part of the UW. All the best.

Very good explanations and well-thought out assignments and practical exploration. The usage of the proprietary GraphLab software was a minus, but since it was used just for exploratory purposes, no harm done.

This course was very usefull tome as it was implemented in a way that it's easy to understand the core of the module and the subject.

I understand and it prepared me for the rest of the Machine Learning courses

This was a great intro course to the topic, and the instructors both make complicated concepts accessible. For example, the explanation of non-linear features in deep learning is extremely clear and intuitive.

This will be really helpful for someone who really wants to start the ML journey and not sure where to start. The content was designed well to suit people across levels and technologies. Strongly recommended.

To define how machines can learn, we need to define what we mean by “learning.” In everyday parlance, when we say learning, we mean something like “gaining knowledge by studying, experience, or being taught.”

Pretty Cool as Foundations in ML!!! If you already have expertize on Pandas and python you would find this course as a good entry point for Machine Learning as the point of view is 50/50 theorical/practice.

Friendly introduction to basic concepts and how to put them in practice to start diving into the exciting ML world that is all around us nowadays, specifically during current uncertain and challenging times.

I appreciate the effort you kept for this online course.Actually I enjoyed learning here.But you can be little bit more detailed in the ipython notebook code explanation. Otherwise ,this is the best course .