Very informative in basics of Machine Learning. It sets the stage for a deep dive into the topics of machine learning like Regression, Classification, Clustering etc.

This course is really useful, as a overview of the whole specialization. The quiz for theory and python implementation strengthen the key points for each module/week.

A perfect introduction to ML. Couldn't ask for more. 6 weeks of coverage is neither shallow nor too deep. Sets up the stage nicely for a deeper dive in next sessions.

Great course to begin your journey into ML

Briefly introduces each topic to give a jist about it and also provides a good starting point for using python in ML context

In a couple of Case Studies it gives you a wide idea about the almost unlimited potential of Machine Learning while it encourages you to learn more and more about it.

it was really amazing to learn from these mentors. They were really humble, clear and had an interesting way to teach. I would love to attend more of them in future.

This course gives a really easy but clear concept for machine learning with examples! I hope I can learn something further with other courses in this specialization.

Fantastic course, great 'learn-by-doing' introduction to ML, really entertaining teachers kept me alert throughout each session. It was great fun and I learnt a ton!

The course is very well structured, interesting lectures with real-life applications, and the programming examples and assignments were very useful and instructive.

A great foundational introduction to Machine learning concepts. Ideal for people that have some background in maths and programming but no career in this direction.

This is a brilliant stepping stone for Machine Learning world. Basics are being discussed and explained in a very simple manner. thanks to the teachers and Coursera

Fantastic overview of various machine learning methods. Very interesting way of exposing concepts using case study approach which makes it more engaging and useful.

This course is a wonderfull introduction to the Machine Learning. It provides a good start point which is very helpful with the other courses of the specialization.

Best course in data science out there. Believe me, I did all 4 other Specialization, some of then very good, some of then no quite si, but this one is far the best.

A great introduction. Good relevant examples and thoughtful data sets were provided for the exercises. Both lecturers were engaging and clearly knew their subjects.

The course is easy and good for beginners. Specifically, case study approach is quite good and furthermore hands on practice assignments greatly improves learning.

Excellent course, gives good overview of all the different ML algorithms.

Case studies and assignments are really good and help a lot in understanding the concepts.

This course builds a quick but engaging overview of machine learning. The structure is more than amazing, the style of teaching is very narrative and very helpful.

It is a great course to learn the fundamentals of Machine Learning, also Emily and Carlos are excellent tutors, they explain very well and they give good examples.

Good course. Strongly application-oriented, intermediate level but with some basic points as well. I think the course could improve a bit on the theoretical side.

Amazing course. It has given me good base knowledge of ML algorithms, I consider the course a "map" for further exploration of Machine Learning world. Good start!

Thank you very much for this course! I really appreciate the effort put into the notebooks and the preparing of the data, and the graphlab library is really cool.

the course is great. I love case study approach for the machine learning foundation. I really love to go on learning in the following parts of the specialization.

I believe it would be very useful if the authors could provide a lecture review about python commands. Specially the ones they are going to use during the course.

Excellent, practical introduction to the topic -- particularly by using use cases that everyone can relate to in order to understand how machine learning "works".