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.

The 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 is incomplete on regression analysis. Also, the grading scale was biased after putting in a lot of time and effort(20 pages). The reason was I didn't follow the assignment questions.

Really difficult. Exams are not fully fair. Example: First exam in Week 3 - including videos 1 to 4. There is one question which answer is in video 8. And so many examples like this one.

Slides in videos are not provided.

It is actually disguisting course. Simply reading the powerpoint without any clear explanation. So bad

I have seen various courses on machine learning and linear regression. This course has been one of the best courses in this field. It provides great detail in the theory and addresses important issues in the field regarding features engineering, regularization, and Ridge, LASSO, and Elastic net models. It also has great practice labs.

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.

Pretty good course, now I have all required knowledge to make a regression and solve some problems like underfitting and overfitting. But it had only 4 machine learning algorithms (Linear Regression, Lasso, Ridge and Elastic Net).

this course fantastically awesome and we can learn machine learning i this course upto the core knowledgr with the help of this course i would strongly recommend you to join this course to gain knowledge rearding machine learning

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

This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.

Great Course curated by IBM team. It is really designed well and helps to achieve the goal. It is as per the industry standard, and practical. One can do this course thoroughly and get a job.

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.

It was a great learning experience with in-depth knowledge and practice-based demos helped me to understand the concepts easily.