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Вернуться к Supervised Machine Learning: Regression

Отзывы учащихся о курсе Supervised Machine Learning: Regression от партнера IBM

Оценки: 125
Рецензии: 30

О курсе

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....

Лучшие рецензии

15 нояб. 2020 г.

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

6 нояб. 2020 г.

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

Фильтр по:

1–25 из 32 отзывов о курсе Supervised Machine Learning: Regression

автор: Christopher W

25 янв. 2021 г.

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.

автор: Nick V

16 нояб. 2020 г.

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

автор: Abdillah F

7 нояб. 2020 г.

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

автор: Nancy C (

24 апр. 2021 г.

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.

автор: michiel b

15 февр. 2021 г.

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.

автор: serkan m

3 мая 2021 г.

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.


25 мар. 2021 г.

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

автор: Konrad B

13 дек. 2020 г.

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

автор: Nandana A

28 дек. 2020 г.

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

автор: Ranjith P

13 апр. 2021 г.

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

автор: Luis P S

4 мая 2021 г.

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

автор: Vivek O

10 апр. 2021 г.

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

автор: Wissam Z

6 июня 2021 г.

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

thank you

автор: Goh K L

5 июня 2021 г.

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

автор: Patrick B

16 июня 2021 г.

Great way learn about machine learning development of regression models

автор: Juan M

11 июня 2021 г.

Very well structured course, the explanations were very clear.

автор: My B

14 апр. 2021 г.

A well structured course with useful techniques in real life.

автор: Ana l D l

21 июля 2021 г.

like that it uses math and also use programming

автор: Nikolas R W

24 дек. 2020 г.

Great course to learn about regression!

автор: Alessandro S

15 апр. 2021 г.

Very well organized and explained.

автор: Rorisang S

4 мая 2021 г.


автор: Volodymyr

15 июля 2021 г.


автор: Hossam G M

22 июня 2021 г.

This course is very great. it focuses mainly on codes and how to get your models trained well with the best results. and for that a prior knowledge of the algorithms and the coding language in addition to the different libraries would be better.

автор: Gianluca P

4 июня 2021 г.

very clear contents and explanations. Regression methods are thoroughly explained. Examples of coding are indeed a very good basis to start coding on the project.

автор: Pankaj Z

19 апр. 2021 г.

Very helpful course. There are few ups and downs but overall its helpful.