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.

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.

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

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.

This is a perfect course for learning and implementing supervised machine learning for regression tasks in Python. However, it does not have a comprehensive explanation of how linear regression and regularization work behind the scenes. This course should be complemented with DeepLearning.AI and Stanford University's supervised machine learning course for more in-depth knowledge about the algorithm.

Pretty mediocre. Quizzes are jejune and sometimes quite sloppy. Most of what I learned actually came from Googling--a common Coursera complaint, I realize. This course isn't awful, but I definitely would not pay for it.

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 a very bad course. I am sorry, but you are not clear enough with the theme. I have read every notebook and it is missing a lot of information.

The instructor was very bad. He was only reading the slides without making any further examples or explanations.

Very hard to follow because the instructor is just reading the blurry powerpoint.

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.

With my background in regression, this course helped me implement what I learned in Python. If you are unfamiliar with machine learning, you should take Andrew Ng's machine learning free course on YouTube first (this is very good course for beginner).

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.