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Вернуться к Multiple Linear Regression with scikit-learn

Отзывы учащихся о курсе Multiple Linear Regression with scikit-learn от партнера Coursera Project Network

4.5
звезд
Оценки: 334
Рецензии: 55

О курсе

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....

Лучшие рецензии

HP
15 сент. 2020 г.

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

IB
7 февр. 2021 г.

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

Фильтр по:

1–25 из 54 отзывов о курсе Multiple Linear Regression with scikit-learn

автор: Mayank S

29 апр. 2020 г.

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

автор: Roland N L

12 нояб. 2019 г.

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.

автор: Nicholas S

13 мар. 2021 г.

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

автор: Hector P

15 сент. 2020 г.

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

автор: Ibtisaam B

8 февр. 2021 г.

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

автор: Zahid Y

23 мая 2020 г.

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

автор: Diego R G

31 мар. 2020 г.

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

автор: Mohammed A S

29 мая 2020 г.

Very good learning guide, thanks for the real project.

автор: mdasif r e

1 мая 2020 г.

NICE GUIDED PROJECT BUT TOO SHORT

автор: Ana C d A M

27 окт. 2020 г.

Good teacher and explanation!

автор: Hafizah A R

30 мая 2020 г.

This is awesome!! Thank you!

автор: Agnim s

16 июля 2020 г.

very fruitful for beginner

автор: MALKAREDDY K R

5 мая 2020 г.

Very informative vedios

автор: Rajkumar R

5 окт. 2020 г.

Good guided project

автор: Senthilvadivel S

16 июня 2020 г.

Amazing explanation

автор: Doss D

14 июня 2020 г.

Thank you very much

автор: Gangone R

4 июля 2020 г.

very useful course

автор: Suci K P

22 июля 2020 г.

it's very clear

автор: Nandivada P E

15 июня 2020 г.

nice course

автор: Carlos M C F

20 авг. 2020 г.

thank you

автор: Anitha V

10 июля 2020 г.

EXCELLENT

автор: Julio T

11 сент. 2020 г.

Excelent

автор: Aniruddh M

29 июля 2020 г.

Amazing!

автор: MD Z A E 1 V C

2 мая 2020 г.

#Awesome

автор: Pulluri R

6 мая 2020 г.

Superb