I'll definitely keep learning the next course.

Some people criticized about graphlab (I thought they should offer 2 versions like RStudio instead a limit-free one. Although I feel comfortable when using graphlab, I'll still use scikit-learn after finishing all courses because it is free and I just use for personally.) but you can use scikit-learn to pass this course (although you have to spend more time) , so this is not a sufficient reason to not giving 5 stars for me.

Great course.

I took and finished Andrew Ng ML course before and I though I 'now i know something about ML', after finishing this course I feel less confident and I can see how many things there are ahead to learn. Especially when it comes to relation between size of sets vs features / model / tuning parameters of model. How much different prediction you can get with the same data!

I can't wait to next part because after Andres Ng's course I started mini project using classification.

Amazing course - the material is taught at a good pace, and with sufficient depth. The assignments are a little confusing though - between pandas and Graphlab, it gets tough to figure out what to take as reference (the iPython notebook uses Graphlab whereas the course page uses pandas/sci-kit). There are differences in language and input values for the two, and it wasn't mentioned anywhere so it took time getting used to. All in all, great course! Thanks :)

Excellent. My favourite machine learning course since Andrew Ng's class. Thorough treatment. Took us from easier hand-holding to deep in the implementation details. Talked both about theoretical considerations as well as practical fine tuning. Would maybe liked to have seen a bit more talked about the problems with data that can affect model fit (multicollinearity / skew / etc) but time constraints don't allow it in an already excellently "meaty" course.

Truly enjoyed this course! The hands-on approach is the best for deepening the understanding of the concepts and applying theories to real problems.

The 'check points', such as 'should print 0.0237082324496' ,in the jupyter notebooks are extremely valuable when other type of help is hard to obtain.

I would take classes like this in the future. Maybe, I will do a search on line to see what turn up as the closest neighbors of this course :)

THANK YOU!!!

I've studied regression and other ML concepts in so many ways, but never have I been able to understand the concepts as I did after auditing this course. I learned the following the hard way: If you want to really get an intuitive, theoretical & practical understanding of ML, you have to listen to a statistician! If I were to realize this fact earlier, I would've never jumped into ML without a degree in statistics. I do highly recommend this course.

It's a truely amazing course. Having studied so much econometrics from undergraduate to PhD, I still learnt so much from this regression course. This course teaches me regressions in a way that is very different from any economics/business schools I have ever attended. While it is technically less demanding than most econometric courses from second year (UG) onwards, it is the applied/practical nature of this course that makes it so valuable.

Amazing - way more depth than the first course, and much narrower focus. Emily teaches all courses here and dives into the math and usage. Programming hints are given but no more walkthroughs of the code. Assignments laid out such that you need to code the algorithms correctly in order to pass assignments. Emily has an excellent way of explaining the math/calculous/reasoning behind the algorithms and proofs thereof. Love it.

This is one of the best online courses out there and not just about Machine Learning. The course was very well organized and the teaching staff was very helpful in resolving whatever issues cropped up. I would suggest you to provide additional readings/ references at the end of the course in 'Closing remarks'. Thank you Profs. Emily and Carlos for the wonderful course. Keep up the good work! I am looking forward to the next one.

This is an excellent course! Emily Fox does an excellent job at explaining what could be a hard concept grasp. I am talking about convex optimization and the LASSO solution. I have taken graduate level classes in convex optimization and the math is high level and can be challenging. The animation Emily presents along with the geometric intuitive explanation drives the intuition home. Thank you Emily and Carlos for this class.

Applications and examples are well-chosen. The choice of theory is appropriate given the audience. The problem sets are a tad on the difficult side in that extreme care must be taken to get the right answers. Some of this has to do with how the assignments are structure. Instructions need to be read several times, which can be quite tedious. In the end, they help you learn the material and force you to implement carefully.

Awesome. You need a little bit of experience but things are explained really well. So glad I took this course, I tried another one from another university, it was disastrous. It certainly helps when you know how to do programming as this takes a lot of time and can be frustrating if you are new at it. Still worth learning it this way. Would recommend to use the GraphLab and maybe later redo it with standard Python tools.

Fantastic course in regression, taught with the mathematical rigor necessary to really understand (not just use) the concepts. The instructors both do an amazing job introducing the concepts piece by piece in a logical and easy to follow manner. In addition, several modules have *optional* in depth derivations of key formulae for those who want to understand the mathematical underpinnings of the regression methods

this course offers so much that by the time you are going through the lecture videos and the reading material, you do all the tasks along and you don't want the lecture to end. In fact by the time a lecture is finished, you want to do more and you click on the next one. the course gives a very good understanding of machine learning models and the skills gained can be used in a lot of different situations.

This course is very detailed and have lot of information about regression, should be taken by anyone who wants to become master in it. But each lesson should be given a week, otherwise it becomes over whelming. Assignments are good as well, though some of them should have better instruction.

There should have been a programming assignment on kernal regression as well, as it is one of the upcoming technique.

Professor Fox is explaining the main algorithms (gradient / coordinate descent) in a clear and understandable way. Quite often, in blogs and reviews, Andrew Ng's course (at Stanford) is mentioned as the reference, to me it looks like these series of courses can match Ng's course on machine learning (using Octave). Being based on Python I would give the advantage to this course and recommend it.

Really great explanations for complex and important principles as well as math approaches and tools. Being a mathematician, I thought that in this math aspect there would be nothing new for me, but still it was a great refreshment and very useful explanations to understand how those methods actually work for machine learning tasks. Great balance of theory and practical applications! Thank you!

This a well thought out course. From the simple concepts it gradually takes you to the more complex ones. The quizzes and programming assignments help you to really understand the problems that were introduced in the videos. The video slides of every module can be downloaded as a pdf document which makes the material easily searchable. And last but not least Emily Fox is a great instructor.

I learned quite a lot during this which I can use in practice. Everything is well explained in the video's. If I had to call a down side then I would say that I had a hard time with the math. This is because I never did something matrices and linear algebra. For those people who miss this background info I would like to say: if you're dedicated then you should be able to survive this course!

Very good course with detailed explanations, both great lecturers, lets you choose environment of your liking for the assignments(python and graphlab are preferred). The explanations are detailed and clear and assignments are very practical. One of the best courses and Specializations on the Coursera I have taken so far. If you contribute lot of time and effort, you will learn a lot.

Awesome. Pure awesome. Great presentation on the theory and all the assignments force you to code solutions from scratch, you're not dependent on Graphlab. Very detailed presentation of advanced topics not covered in other superficial introductions to regression. And practical advice from the instructors shows that they are imparting practical real-world advice on running regression.

Congrats to Carlos and Emily on producing a great course. As a humble software developer with no statistics background (and someone who hasn't used calculus since they left school nearly 30 years ago) I found this course to be very accessible, the concepts clearly explained, and the results of the course work have been rewarding. Thanks for kick-starting my little grey cells again.

I found this course really well presented and structured. II am currently developing myself a course on models and data and I have found many good ways of teaching in the presented content. The course could be even better if mentors were more responsive on the forum, as it is the case for most Coursera courses. I would encourage Coursera to take extra care of this kind of issues.

I really enjoyed this course on regression. The teaching was second to none and the course material was excellent. The assignments were relatively challenging but the slides and videos did a great job of boiling down difficult ideas into intuitive steps; they really helped! I will recommend this course to friends and colleagues interested in learning about regression and ML

Great course that guides you to coding regression (linear, polynomial and ridge regression with gradient descend, lasso with coordinate descend, and linear-average kNN), as well as demonstrating key statistical concepts in the slides and while doing the exercises. This code deepens understanding of key regression algorithms through a hands-on, learning-by-coding approach.