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學生對 Rhyme 提供的 Project: Predict Sales Revenue with scikit-learn 的評價和反饋

23 個評分
5 條評論


In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. By the end of this course, you will be able to: - Explain the core ideas of linear regression to technical and non-technical audiences - Build a simple linear regression model in Python with scikit-learn - Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas - Evaluate a simple linear regression model using appropriate metrics This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, 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, you’ll get instant access to a cloud desktop with Jupyter 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....

1 - Project: Predict Sales Revenue with scikit-learn 的 5 個評論(共 5 個)

創建者 Budi S

Mar 14, 2020

awesome with this project course

創建者 Etape M

Mar 07, 2020

I loved his explanations.

創建者 shiva s t

Mar 16, 2020

great course

創建者 Karim E

Dec 05, 2019

just need to illustrate how to judge the error

創建者 Apurv G

Mar 30, 2020

Its not a Project. Its just a small course about liner regression. may be good for new learner but if you already have a knowledge of regressions in python its waste of time. and the tool used is virtual pc which is very slow 10 min video takes 1 hour to complete.