Visual Machine Learning with Yellowbrick
3587 ya inscrito
3587 ya inscrito
Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning. 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.
Data Visualization (DataViz)
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por RR9 de jun. de 2020
I really enjoyed this project. Thank you very much for your valuable teaching. Like to learn more from your end.