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Opiniones y comentarios de aprendices correspondientes a Multiple Linear Regression with scikit-learn por parte de Coursera Project Network

4.5
estrellas
333 calificaciones
55 reseña

Acerca del 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....

Principales reseñas

HP
15 de sep. de 2020

This project is great. Clearly explained and well delivered. I will highly recommend to take this project. The instructor is great!

IB
7 de feb. de 2021

Well paced, very informative, I felt I learnt skills that I can apply to practical problems immediately.

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1 - 25 de 54 revisiones para Multiple Linear Regression with scikit-learn

por Mayank S

29 de abr. de 2020

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

por Roland N L

12 de nov. de 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 Nicholas S

13 de mar. de 2021

I highly recommend any project from this instructor, he clearly defines all goals and the steps to get there, provides numerous examples, and simplifies complex concepts so that those with little to no experience could understand. I take all of his projects, 10/10

por Hector P

15 de sep. de 2020

This project is great. Clearly explained and well delivered. I will highly recommend to take this project. The instructor is great!

por Ibtisaam B

8 de feb. de 2021

Well paced, very informative, I felt I learnt skills that I can apply to practical problems immediately.

por Zahid Y

23 de may. de 2020

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

por Diego R G

31 de mar. de 2020

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

por Mohammed A S

29 de may. de 2020

Very good learning guide, thanks for the real project.

por mdasif r e

1 de may. de 2020

NICE GUIDED PROJECT BUT TOO SHORT

por Ana C d A M

27 de oct. de 2020

Good teacher and explanation!

por Hafizah A R

30 de may. de 2020

This is awesome!! Thank you!

por Agnim s

16 de jul. de 2020

very fruitful for beginner

por MALKAREDDY K R

5 de may. de 2020

Very informative vedios

por Rajkumar R

5 de oct. de 2020

Good guided project

por Senthilvadivel S

16 de jun. de 2020

Amazing explanation

por Doss D

14 de jun. de 2020

Thank you very much

por Gangone R

4 de jul. de 2020

very useful course

por Suci K P

22 de jul. de 2020

it's very clear

por Nandivada P E

15 de jun. de 2020

nice course

por Carlos M C F

20 de ago. de 2020

thank you

por Anitha V

10 de jul. de 2020

EXCELLENT

por Julio T

11 de sep. de 2020

Excelent

por Aniruddh M

29 de jul. de 2020

Amazing!

por MD Z A E 1 V C

2 de may. de 2020

#Awesome

por Pulluri R

6 de may. de 2020

Superb