it gave more details than my class room. it also adopts a practical approach to learn. it is a great course in regression especially for practitioners.

Thanks Carlos and Emily :)

Great course on Regression. This will help build basic for upcoming modules. Emily teaches the concepts in a simple way. I liked the structure and coverage of Regression topic.

Great material, this was tougher than the previous course. It is challenging and more exercises to practice which help to a better understanding of the concepts. Great mentors!

An extremely well designed course, I am an instructional designer myself, so adding weight to the words. Would have appreciated a few more assignments for the last week though.

I feel I understand regression models better than before. But I still need to read more books on the same topic to actually convert what I learned here to long term memory :)

Fantastic Course, allowed me to gain insights to regression. Both the instructors like always have been excellent. Shout out to coursera for allowing me to take this course!

It's a really nice course.What i've learned in this course: how to implement a regression model through my own hands, assessing performance, feature selection...and so on.

This course is very well organized and all the information is relevant. Everything is explained in great detail. The exercises really make you feel that you are learning.

All the courses in this specializations are very well-made and rigorous. I think all MOOCs, especially techinical ones, should be as well-designed as this or even more.

The course is really helpful. It has started with simple Regression model and gradually build the different advance regression model. Thanks for this wonderful course.

rigorously explained some of the most important algorithms in regression world, also the pros and cons of using certain algorithm for certain conditions. totally worth

Good overall theoretical and practical explanation of the material, I was also able to use scikit learn and pandas without any difficulties instead of graphlab create.

Great class (really liked the graphical interpretations of Lasso and Ridge optimizations).

Perhaps some quizzes (and especially assignements) could be more challenging?

Great course, excelent professors & simple yet accurate explanations, always guiding you through the course and through practical implementation of acquired knowledge

Great course! I love the instructors and the thoroughly designed structure of their course. The effort they put into this course certainly shines through every video!

I love this course! It explains more about Regression itself and not just discussing on how to use libraries for it! Very intuitive and informative at the same time!

its a great course. little bit disappointed from the decision of not continuing Recommended systems and capstone project. PLEASEEEEEE roll out course 5 and 6 as well

Nice course. I started with this specialization as a beginner. I was very intuitive and great course I would recommend to others people interested in data science.

The Regression course is pretty amazing. Got to learn a lot of cool stuffs. Emily Fox made everything clear. Glad to have taken this course and the specialization.

Coursera is shaping me in the best version of myself through knowledge and guide. I am always be grateful of god that I found Coursera. My online teaching guru!

Great course - you'll become much more accustomed to Python if you aren't already (I'm an R convert) and really learn the principles behind regression analysis.

Covered a lot of the common practical aspects of regression modelling and also covered the calculus derivations for those who are curious. Great course overall.

Very clear and thorough explanation on regression and implementation details. The closed-form calculation and comparison against gradient descent is excellent.

I love how this course goes deep into the math, yet makes it quite approachable even if you have no math skills. Emily is so good at explaining the concepts!

Great background through applications of linear regression and explanations that are step by step that allow the understanding and construction of learning.