Better than the first one, however I feel that some material was missing and that the last week was rushed. It is sad the don't talk about XGBoost and other recent algorithms for regression (Elasticnet and so on). I also think they should only focus on using Numpy/Scipy instead of closed source software they recommend.

It was a great learning, concepts are explained in so simple manner. Thank you.

On the exercise, i think it would be better to test concept than the actual result value as different people uses different python packages and sometime result may differ slightly which make it difficult to pass. Overall simply Great!

Good introduction to linear regression. The quizzes are moderately difficult, and can be somewhat time consuming. There is a lot of depth to this topic, and tho this course touches on a lot of it, it won't be enough to get a very thorough understanding. In general I'm really glad I took this course

The course was mostly well taught, and having the two programming assignments each week was useful. One so you could learn the technique, and another so you could learn the underlying algorithm. Some of the topics were occasionally light and too fast on the detail, but overall a very good course.

This course is better than the previous one is this specialization the video lectures very good and topics but programming assignments looks to designed to use sframe and graphlab so it has some problems but this time there is better supporting for sklearn and pandas so i recommend this course

The courses get better and have more assistance for those of us who can't / didn't use graph lab. It is still outdated as python 3 came out after the course was created. But did learn a lot of stuff. Module 4 was the most frustrating as you'll get the wrong answers if you use pandas/sklearn.

Good course that covers the intuitions and goes a bit deeper with the implementation. Main down point is the fact that they try to get you to use graph frames and the alternative pandas route is often a bit of an afterthought, occasionally leading to trouble/confusion with certain quizzes.

The course dealt with implementing the key regression algorithms from scratch as well as tuning the hyper parameters using cross validation and observing what the pros and cons of each algorithm was.The course explained the maths/stats knowledge well and I would recommend it.

Once again, what a simple way of presenting concepts of gradient descent, coordinate descent. One thing to improve however would be to present the results of the quizzes when they're validated, will all the right answers, along with explanations. Thanks once more!

Amazing course uncovers all the abstraction behind Regression . Presented all the mathematical algorithms in such a convenient manner that results in efficient coding. Provided Notebooks are very well designed and presented every coding method in an easy manner.

The videos are great, well-structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

Excellent course overall. Only issue was the inaccurate instructions for those using sklearn instead of graphlab (with graphlab it's very easy to do, but a bit painful for those like me who like to write a program instead of doing in interactive mode only).

The design of the course and presentations are great. It was very useful for my career development and fun. But, I think that the material is outdated and need a major update, especially Python packages and codes. Also, the forums are not active anymore.

Some of the quizes and assignments had grading issues but I thought the curriculum was great. All of the optimization techniques learned to solve for regressions seem like they could be useful in other areas of machine learning and other applications.

In the end of the course it became a bit boring to work with the same dataset over and over again. But I learned a lot about such algorithms as LASSO, Ridge regression. I guess that overall there's a good balance between math, theory and practice.

Excellent lectures, evenly paced and nicely balanced between theory and practice, and mostly great quizzes and practice problems. However, the complete lack of instructor participation in the forums leaves many student questions unanswered.

Like all coursera courses, inadequate guidance in notebooks to prepare for tests. No feedback or guidance on instructions when you get stuck (e.g, write this function to do thie following, no way to help figure out how!)

The course was very helpful. It would have been better if the tutors explained how they have arrived at some values they used like initial weights, tolerance, threshold etc.., so that the learning is comprehensive.

The course was excellent. It helped me understand the basic concepts of regression. This course helps us think why and when a model needs to be used which I think is more important in the current day of big data.

Useful to get a first understanding but do not feel comfortable to use any of it in real case scenarios. Could give solutions at the end of the whole course to see best coding, and unsolved questions.

It would have been nice to have some videos devoted to inference on the model coefficients and statistical model comparison, or on how adding/removing features can be assessed from such point view.

Programming assignment sometime ambiguious and hard to follow. A lot of time you have no idea WHICH dataset they are talking about e.g. "query house" in the last lesson.

Overall it's a great course.

This course start from problems. So this great to motivate the content and let student know why. However, there are lot of confusion questions that lead to miss understand the exercise problems.

I appreciate the nuts and bolts focus on implementation that facilitates development of intuition, intuition that for me at least does not come from presentation of the mathematics in isolation.

This is an excellent course to get the math involve behind the regression. Instructors are awesome. I also feel that Bayseain regression should have been included. I missed that part badly.