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Predict Sales Revenue with scikit-learn

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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.

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"Predict Sales Revenue with scikit-learn" is a 2-hour project-based course focused on data science and AI, led by Coursera. It teaches linear regression using Python, employing scikit-learn for modeling, pandas for data management, and seaborn for visualization. Learners will predict sales revenue from advertising data, gaining skills in EDA and model evaluation. Accessible via Coursera's Rhyme platform, the course offers instant access to pre-configured cloud desktops, making it ideal for beginners in North America. Suitable for foundation-level learners with a starter subscription.