Great course, very detailed and hands on, also including appropriate amount of mathematical rigour to help you understand what is going on under the hood. Highly recommended. I specially liked the modules on Ridge regression and Lasso regression, really well done.

I found this an excellent introduction to the topic with a good mix of well-presented material and practical application using the IPython notebooks. I would love to have the course finish with a project where we apply the learned methods to a different data set.

Very responsible teachers and practical classes content.You can not only learning the ML theory from scratch,but also learn to implement the algorithm using python by yourself.This is the best ML course I ever seen.

Thanks for the teachers' hard-work.You are great!

Excellent. This is an ideal course in order to understand various aspects of regression techniques. Explanation using hands-on exercises helps me learn this course very effectively. I must appreciate the efforts of both Instructors (Prof. Emily & Prof. Carlos).

I felt that the course was detailed and contained significant in-depth study about regression techniques. The assignments were well designed, starting from a single step and eventually enabling the candidate to be able to write the complete methods on his own.

This is probably the best Coursera course I have completed so far (and I am kind of Coursera junkie). very well structured, the right amount of math and driven by the experiments on the real data.

Looking forward to Classification course and others in series.

The regression is done very well. I love the tutorials especially, they are very clear with good test feedbacks on some of the latter week contents. If you want to get into machine learning, this is a very important part to help you with all the other parts.

I liked pretty much all of the content.

The lectures are detailed.

The assignments helped me understand the techniques used in regression. The step by step approach is great.

What I dislike a bit is the promotion of proprietary and expensive Graphlab software.

A few minor glitches with the homework assignments so far. Hopefully this is only because it is the first time the class is being offered.

I love the instructors. Great enthusiasm and both clearly love what they do. Inspiring for data scientists in training.

This is a great course! The course is easily understand, the lecturers are very nicely talking in the videos to show you the knowledge of regression. The assignments are designed in a way helping you learn, practice and implement the regression algorithms.

Really exciting course. The concepts are well explained and implementing algorithms by myself is really a inspiring experience. It is really a pity that the last 2 courses in the specialization were canceled. I am even willing to pay them for 100$ each!!!!

Pretty challenging from a mathematical perspective, but extremely interesting and well-explained. I was glad to see there were plenty of opportunities to use Pandas and other Python libraries instead of just relying on Graphlab. Very happy with this class.

Excellent course. Highly recommended. Emily Fox is clear and comprehensive. In addition, this module's exercises can be fully completed using Python's Pandas sklearn and numpy libraries and without requiring the propriety GraphLab library. Good work!

Good insight into regression models. You will dive into the details of implementations of Lasso and Ridge regularization techniques. The course is actually easy to grasp for graduates with technical background, never the less gives good knowledge.

Very nice course... The instructors were really great, the explanations, the presentations, even the color schemes were all really great... Definitely one of the most fun courses I've taken at Coursera... The assignments were also well designed...

The best Machine learning course I ever took. I compare it very favourably to Jeff Leek's course, or Andew Ng's course - which are both good in their own right.

A lot of effort went into making this a really good course. I very much recommend it.

An incredibly good approach to regression. It is the perfect continuation of the introduction course, it provides very good skills to solve regression problems. I´m eager to start with the third course of the specialization after this one!

All concepts are explained really well! Knowing all the mathematics behind machine learning can never hurt, but when it comes down to actually implementing something useful it all boils down to the practicalities of the implementation.

Wow.... to complete this course, one really needs to work hard... one of the best teachers and the way they build concepts, so easy and systematic... thanks you so much for making me learn some of the challenging concepts with ease...

Great course and great instructor. Course covers regression models in great detail. The instructor's explanation of concepts and intuition behind why things are the way they are was really helpful to learn and appreciate the concepts.

A really nice course, explanations in the videos are absolutely clear. I do have to say, however, that I was hoping to go into kernel-based regression a bit further. But overall, a great course which i definitely recommend to others!

One of the top Coursera courses I've had the pleasure of taking!

The instructors do a great job of making the math understandable (although I am a graduate student in applied math, so the mathy parts of machine learning aren't new).

Highly recommend this course if anyone wants to truly understand the stats used behind regression. Professor Emily has taught this specialization in the best way possible. Thank you Cousera for providing such specialization online.

Great course! Excellent overview of the goal of regression, and the difference between L1 and L2 regularization, as well as some generally applicable machine learning concepts/algorithms. Packed with material and very worthwhile.

Excellent course and requires some hardwork during weekends but pays off very well. It teaches Liner Regression, regularization, loss fn and k-NN among others - all very important ML concepts.

Thank you to excellent teachers!