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Perform Feature Analysis with Yellowbrick

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Welcome to this project-based course on Performing Feature Analysis with Yellowbrick. In this course, we are going to use visualizations to steer machine learning workflows. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. 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, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn 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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Join this professional course to master feature analysis with Yellowbrick, focusing on visualizing machine learning workflows. Predict room occupancy using passive sensor data like temperature and humidity. Offered by Coursera, this 120-minute hands-on project utilizes Python, Jupyter, and scikit-learn on a pre-configured cloud desktop. Ideal for North American learners, access instructional videos anytime and the cloud desktop up to five times. Perfect for big data and analytics enthusiasts.