Really good course but it is whistle-stop through the methods. I strongly recommend getting a book to accompany the course if you are relatively new just so you can cross reference some of the methods and functions.

I found some of the examples a little more difficult to apply to the course work because of how they were demonstrated in the lab. This is NOT a bad thing, all good learning, but when you're trying to unpack things it's good to have another reference source handy.

Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

Learned really about supervised learning and more importantly regularization and some available methods.

I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!

very detailed. However, it is better if the gradient decent has its lesson.

Great course! Covered everything I wished to learn!

Before taking this course, I tested similar courses offered by other institutes or universities. I am glad that I chose IBM because it has a good balance of concepts and applications. I learned a lot from this course. and will be using what I learned in analyzing experimental and survey data.

I gave this course a 4 instead of 5 because there was insufficient explanation on the different evaluation metrics.

Good overview of the different regression models and the theory behind them. Could be a bit more attention to common pittfalls and type and size of problems which are usually addressed by these methods.

T​he balance between theory and application is such that both are left quite poorly covered. One does not get an understanding of how algorithms work, explanations focus on 'intuititve' understanding. At the same time, the coding part is not particularly detailed, either. Moreover, there are several mistakes in videos, quizzes and jupyter lab books. I would not recommend this course.

The course covered various aspects of regression modelling in good detail and the practice notebooks were also very helpful in implementing and reinforcing the learnings of course. Though the subject matter is quite wide, efforts were made by the instructor to cover most of them.

Thanks very much for this great course. It is comprehensive and intuitive in terms of Regression analysis. It covers all the necessary tools for an essential and sufficient application of Regression analysis.

Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of basic regressors is gained from this course.

It was an exceedingly difficult for me, sometimes JSON files under Jupiter Notebook links made me freeze. But this intensity of challenge brings me an improvement for my skills.

Thanks Coursera & IBM

Linear Regression, Ridge, Lasso, Elastic Net, L1 and L2 regularizations... All very well explained theoretically and coded on Jupyter Notebook accordingly.

The instructor from videos is amazing. Great tutor. So far the courses from IBM Machine Learning Professional Certificate are really, really good.

Excellent!!! I rather recommend the course for those who need to understand properly and fast!

Very well presented. This is without doubt the best series for Machine Learning on Coursera.

best course ever I learned regression and polynomials in a professional way.

thank you

Well structured course. Concepts are explained clearly with hands on exercises.

Please give the lecturer credit and include him as one of the instructors

really good course, content is rich with good machine learning concepts

Great way learn about machine learning development of regression models

awesome expirence and iam good to go towards an next course thankyou.

G​reat course for someone wanting to learn Supervise Machine learning