A well thought out and nicely paced introduction to Regression following on from the equally good foundation course. I particularly like the way that the assignments assume an improving knowledge and familiarity with Python as the course progresses. It will be interesting to see if the subsequent courses in the specialisation continue in this vein - I hope so. I note the concerns that some have expressed about the use of graphlab.create for examples and assignments, but tend to think there is benefit from gaining familiarity with a number of different data science ML tools and libraries. Also additional code and instructions are available for those determined to use other tools such as Pandas and Scikit Learn.

Nice explanation of concepts, and very helpful with getting started on the programming assignments. The algorithms are explained well in pseudo code, and the instructor does a good job at explaining why they work the way they do. The math is not very challenging, so I never felt frustrated.

I only wish there was not such an emphasis on Graphlab. Although they do allow you to use other methods to finish the assignments, it feels as though more attention is given to explaining how Graphlab works instead of standard, free python libraries. I understand that they're trying to push a product, but I don't want to pay for something I'll only be using for a few courses. More attention should be given to sklearn.

Pros: the instructors and TAs seem to have better experience to manage the course, the lectures are extremely well explained and are very enjoyable, all the lectures last less than 15 minutes, there are some advanced optional videos that are interesting and they are not needed to complete the assignments, the numpy tutorial provided is helpful for the assignments, the programming assignments emphasize the need for testing our code (which is one of the most important skills any developer should have).

Cons: The written instructions on the programming assignments are still a bit confusing sometimes and may need a review for future iterations of this course.

Can't praise enough!

This IS THE COURSE for regression. Cannot believe I didn't stumble upon it earlier enough.

Great Specialization, Great Course, Great Professors, Great thought provoking Quizzes and assignments, helpful mentors and more important that any of the above, amazing comprehensive content covering each and every topic from simple linear regression to feature selection and going all the way to kNN Regression.

An enjoyable and great learning experience.

Hope to carry on the same level of enthusiasm through the rest of the specialization.

Thank You Emily, Carlos, Johan (mentor) and rest of my classmates. And of course, Thank You Coursera.

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job. We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

A very challenging course, but one that provides you great insights. This class focuses on the algorithms of regression and various optimization techniques. The iPython (Jupyter) notebooks lead one though solving the various problems, and luckily I have enough Python background and sufficient mathematical intuition to complete the course. Those without either would probably find the course extremely difficult, but not impossible.

I'm leaving the course with an excellent understand of how regression works, and how I can use Lasso and Ridge regressions to improve my predictions. This class is one of the best that I've taken on Coursera.

The course is well paced, well organized, and employs a rigorous curriculum design that builds idea upon idea. Issues are well motivated and those motivations are used consistently throughout. Unlike other courses I have taken that attempt a survey across a number of areas, this course dives deep into the important topics in regression. The reward is a more thorough understanding of the theory, mathematics, utility, trade offs and implementation details of various algorithms. Moreover, the individual ideas are tied together in an overarching machine learning work flow that applies to many areas and tasks in machine learning.

Alot of information and math, great pragmatic approach in this course. I´m a Little dissapointed that the Multivariate outputs Y where and as I understood is not covered in this Courses, I would like to use that at work to predict several values. The quiz questions was a Little bit tricky, I misunderstood the questions and answered on the wrong data set. Overall the course was not easy and I needed to put in alot of hours to get the coding correct, so be ready to put down some hard work (if you are a slow Learner like me :) ). Anyway great course! And I will jump directly to the next course with Emliy and Carlos.

Very, very good course, high-quality teaching, and well-developed quizzes and assignments. The course is challenging enough to feel motivated and satisfied when finishing a week's work, but not so challenging as to scare people (or me, at least) away. I hope (and trust) that you keep up the good work in the courses to come. If there was one thing I could change, it would be to include a discussion of statistical significance/inference (as suggested by Emily towards the end). It is unclear now how that can be analyzed in the Graphlab environment, even though it is a very important topic for practical purposes.

Emily Foxx's teaching methods in this course are the bomb. She does not give you code hints as Carlos Guestrin would, but rest assured she breaks the concepts down to basic learning blocks and does a pretty neat job at connecting the dots between blocks to present a holistic picture of the course.

I called out her name countless times trying to wade through the programming tasks. Guess that worked for me many times as I imagined her tutoring me in a PhD class and breathing down my neck to meet deadline on pay resit fees (akin to Coursera subscription charges).

Overall, 7-Star Course and Teaching Methods.

I loved this specialization very much !!! Emily and Calors are always very passionate and humor. In this regression course, I have learned a lot of algorithms, which make me understand how the regression functions in the first course( Machine Learning Foundations: A Case Study Approach ) work. Especially, I could contruct a function now by myself. It is really really exciting !!! Emily makes a good job to do some visiualization to make the algorithms comprehensible. But this course is kind of difficult for me and sometimes I need to watch a video so many times to understand an algorithm.

I saw a number of machine courses that are with too general contents and more like conference presentations. It's hard to learn and grasp something from them. However this is a real Machine Course that provides informative, appropriate details and derivations from which I can learn and understand the meaning and insights buried in math symbols and equations. No doubt, the optional video lectures are excellent enhanced "nutrition." Looking forward to the three courses in this specialization. Thanks, Profs. Emily Fox and Carlos Guestrin, for instructing such wonderful authentic courses.

Emily did a great job and presented this course in a very clear manner. I'm in the specialization primarily for the applications of regression tools and not as much for the mathematical theory. But I have to admit, I found it very helpful when Emily went into the proofs and theory behind tools such as gradient descent. She did this in a in a straightforward manner and it ultimately helped me understand the applications better. Carlos and Emily's visual 'movie' of the Lasso convergence was also extremely helpful. I'm very much looking forward to the next course in the specialization.

This is an excellent course. The instructors are very likeable. Each module follows the same outline 1) build intuition with simple graphs 2) introduce the matrix operations geometrically with some clever graphics 3) a rigorous mathematical discussion 4) playing with the functions in an ipython notebook especially focusing on hyperparameters, 5) implementing the regression equations in your choice of programming language. As much as I love Andrew Ng's Machine Learning course, you could take this sequence instead and get more explanation with the same mathematical rigor.

This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter. What really sets this course and series apart is the case-base methodology as well as in-depth technical subject matter. Specifically, the step through coding of the algorithms provides key insight that is seriously missed in other classes even in traditional academic settings. I highly encourage the authors and other Coursera publishers to continue to publish more educational material in the same framework.

Excellent course on the use of regression in machine learning. It does not simply stop with simple linear regression but also tackles ridge and lasso regression using Python notebooks. One big advantage for those not familiar with Python is that the Python notebooks have just enough boiler plate code to make it feasible for Python beginners but not so much that the challenge is gone. The lectures can feel rather technical at times but this, at least in my mind, enhances the course and at no point did I feel I was "drowning" in formulas.

I was only able to complete week 1 to week 3 thoroughly, and random check on other weeks due to limited time at my disposal at this moment.

In general, I found the course to be very interesting and an excellent introduction to building predictive models . Particularly , i appreciate the way mathematical formulations was explained to carry along beginners in this areas.

Nonetheless, I would suggest that the general notation slide in week 2 should include concrete data example in a table to explain the notations ie. x[j], xi[j], etc

I enjoyed the first course in the series, but was slightly worried the specialization would all be too focused on the GraphLab product specifically. This course is proving that Carlos and Emily intend for us to truly understand the concepts and algorithms behind machine learning. For anyone on the fence that is concerned about this, you will learn how to implement machine learning in ANY package. In fact you will learn how to do it with no package at all! Thanks so much to Carlos and Emily and the Coursera staff, this is great!

Excellent course that is the second in this specialization. It goes beyond the Foundations course and delves further into utilizing machine learning with regression based methods. The course also uses Python. There is some requirement that you should have some degree of familiarity with programming, although you can pick up some skills in coding in Python even if you are not familiar with it (- I wasn't familiar with Python much, although I am familiar with other languages).

Overall, highly recommended.

This was a great course. There were a few issues I think with some of the quiz questions and some of the lecture material. However, considering how complex these concepts are, the material was very clearly conveyed overall; and the assignments were very helpful. There seem to be a number of these types of specializations available on Coursera; and they all seem really good. However, I started out with the University of Washington machine learning specialization and haven't looked back. Well done!

This course is great! I had a lot of fun going through the exercises and concepts they show are really relevant. I am not sure about the level of the whole series, as it probably is more towards beginner than intermediate, but it's great to get some practice with Python and learn / brush-up / deepen knowledge in ML.

I am really looking forward to the next class - that's probably the area I would like this series to improve, the gaps between courses are just too long.

Overall great work!

Thanks!

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 :)