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Avis et commentaires pour d'étudiants pour Multiple Linear Regression with scikit-learn par Coursera Project Network

344 évaluations

À propos du cours

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....

Meilleurs avis


15 sept. 2020

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


7 févr. 2021

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

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par Roland N L

12 nov. 2019

par Nicholas S

13 mars 2021

par Mayank S

29 avr. 2020

par Hector P

15 sept. 2020

par Ibtisaam B

8 févr. 2021

par Zahid Y

23 mai 2020

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27 oct. 2020

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30 mai 2020

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16 juil. 2020


5 mai 2020

par Rajkumar R

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16 juin 2020

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par Suci K P

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11 sept. 2020

par Aniruddh M

29 juil. 2020

par MD Z A E 1 V C

2 mai 2020

par F 1 B

3 sept. 2022