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學生對 IBM 提供的 Supervised Machine Learning: Regression 的評價和反饋

250 個評分
51 條評論


This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....




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.


1 - Supervised Machine Learning: Regression 的 25 個評論(共 55 個)

創建者 Christopher W


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.

創建者 Nick V


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

創建者 Abdillah F


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

創建者 Nandana A


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

創建者 Ranjith P


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

創建者 Minh L


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

創建者 Nir C


Great course! Covered everything I wished to learn!

創建者 Nancy C (


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.

創建者 michiel b


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.

創建者 Kalliope S


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.

創建者 Minhaj A A


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.

創建者 serkan m


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.

創建者 Nicola R


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

創建者 Alparslan T


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

創建者 konutek


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

創建者 Luis P S


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

創建者 Vivek O


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

創建者 Wissam Z


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

thank you

創建者 Saraswati P


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

創建者 Goh K L


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

創建者 Pratik N


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

創建者 Patrick B


Great way learn about machine learning development of regression models

創建者 Usman V N


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

創建者 Bishal B


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