Predict Sales Revenue with scikit-learn
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.
Data Visualization (DataViz)
Exploratory Data Analysis
由 HK 提供May 9, 2020
It was just too easy for me! It could have been some more harder and larger
由 SA 提供Jul 19, 2020
This course give enough base understanding on how to work with simple linear regression. The instructor explanation was also so easy to understand.
由 RB 提供Jun 7, 2020
Good Course but you need atleast some idea before hand about machine learning...
由 GR 提供Jul 7, 2020
After I did this guided project, I was able to build simple regression models by applying the skills I learnt.