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Visual ML with Yellowbrick

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This course instructs students on the Rhyme platform of Coursera, where they will evaluate random forest classifiers using Yellowbrick, address class imbalance, and conduct feature analysis with regression, cross-validation, and hyperparameter optimization.

Key AI Functions:Data Science,Python Programming,Machine Learning,Data Visualization,Scikit-Learn

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Description for Visual ML with Yellowbrick

Employ Yellowbrick's visual diagnostic tools to evaluate the performance of a random forest classifier on the Poker Hand dataset.

Address the issue of class imbalance and implement strategies for diagnosing and resolving it.

Utilize regression, cross-validation, and hyperparameter optimization to investigate feature analysis, feature importance, algorithm selection, and model evaluation.

The course will be conducted on Coursera's Rhyme platform, which offers a hands-on experience with pre-configured cloud workstations that include Python, Jupyter, Yellowbrick, and scikit-learn.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 1

Offered by: On Coursera provided by Coursera Project Network

Duration: 44 hours (approximately)

Schedule: Flexible

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