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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.
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1 - 11 von 11 Bewertungen für Visual Machine Learning with Yellowbrick
von Khandaker M A
•8. Aug. 2020
This is a better-planned guided project with practice quizzes which really helps. So I would definitely like to recommend this course for those who wants to have a knowledge on visual machine learning.
von Ramya G R
•10. Juni 2020
I really enjoyed this project. Thank you very much for your valuable teaching. Like to learn more from your end.
von Abhishek C
•10. Mai 2020
but the cloud desktop is not good
von Gangone R
•2. Juli 2020
very useful course
von MUHAMMED S S A
•16. Apr. 2020
It's very useful
von Kamlesh C
•26. Juli 2020
Thanks
von tale p
•28. Juni 2020
good
von p s
•26. Juni 2020
Nice
von sarithanakkala
•24. Juni 2020
Good
von Maxwell S d C
•15. Juni 2020
Nice but short and somwhat lacking theory
von Sujal V
•18. März 2020
good
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