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Вернуться к Supervised Machine Learning: Regression

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

4.7
звезд
Оценки: 147
Рецензии: 33

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

NV
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.

AF
6 нояб. 2020 г.

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

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26–36 из 36 отзывов о курсе Supervised Machine Learning: Regression

автор: Volodymyr

15 июля 2021 г.

Super

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.

автор: El M S

20 янв. 2021 г.

Good course with nice exemple for illustration

автор: Keyur U

24 дек. 2020 г.

A great course to kick start your ML journey.

автор: Bernard F

27 нояб. 2020 г.

An truly exciting course!

автор: Iddi A A

11 дек. 2020 г.

Excellent

автор: YASH A

22 апр. 2021 г.

Nice

автор: Kalliope S

24 июня 2021 г.

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.

автор: Ramesh B

30 янв. 2021 г.

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.