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Voltar para Multiple Linear Regression with scikit-learn

Comentários e feedback de alunos de Multiple Linear Regression with scikit-learn da instituição Coursera Project Network

266 classificações
43 avaliações

Sobre o curso

In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper. By the end of this project, you will be able to: - Build univariate and multivariate linear regression models using scikit-learn - Perform Exploratory Data Analysis (EDA) and data visualization with seaborn - Evaluate model fit and accuracy using numerical measures such as R² and RMSE - Model interaction effects in regression using basic feature engineering techniques This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, this means instant access to a cloud desktop with Jupyter Notebooks and Python 3.7 with all the necessary libraries pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Melhores avaliações


Apr 29, 2020

Good Course. Extended my knowledge to implement multivariable Linear Regression. Thanks.


May 23, 2020

Best Course to linear regression basic to get advanced knowledge in neural network

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1 — 25 de 42 Avaliações para o Multiple Linear Regression with scikit-learn

por Roland N L

Nov 12, 2019

It helps a lot that the programming assignment (= the functions and methods of the various Python libraries for data analysis) is demonstrated in real-time. Thus, one can learn or try to memorize the correct syntax without the need to spend a lot of time to figure out where one forgot a dot, parentheses, square brackets, or an underscore; and focus more on the theoretical model (in this case multiple linear regression) and its related concepts themselves.

por Mayank S

Apr 29, 2020

Good Course. Extended my knowledge to implement multivariable Linear Regression. Thanks.

por Zahid Y

May 23, 2020

Best Course to linear regression basic to get advanced knowledge in neural network

por Diego R G

Mar 31, 2020

Better than the Michigan data science curses by 1 billion miles!

por Mohammed A S

May 29, 2020

Very good learning guide, thanks for the real project.

por mdasif r e

May 01, 2020


por Hafizah A R

May 30, 2020

This is awesome!! Thank you!

por Agnim s

Jul 16, 2020

very fruitful for beginner


May 05, 2020

Very informative vedios

por Senthil v S

Jun 16, 2020

Amazing explanation

por Doss D

Jun 14, 2020

Thank you very much

por Gangone R

Jul 04, 2020

very useful course

por Suci K P

Jul 22, 2020

it's very clear

por Nandivada P E

Jun 15, 2020

nice course

por Anitha V

Jul 10, 2020


por M A M

Jul 29, 2020


por MD Z A

May 02, 2020


por Pulluri R

May 06, 2020


por tale p

Jun 26, 2020


por p s

Jun 25, 2020


por Vajinepalli s s

Jun 16, 2020


por Abhishek P G

Jun 16, 2020


por Katamreddy M r

Jun 27, 2020


por Yogesh P

Jul 01, 2020

The course is well structured and all the important theories and concepts have been explained in a quite detailed fashion. One should definitely try out this course to strengthen their skills in foundational level machine learning.

por Nikhil T

Jul 06, 2020

This course who wants to learn about basics and he has made it understand quite fine and apart from it quiz is very easy so you will pass but i would request him to atleast have 2 or more examples it would be much more better