Interpretable Machine Learning Applications
A brief synopsis of this course is that it covers the development of interpretable machine learning applications utilizing random forest and decision tree models, emphasizing feature importance analysis for responsible machine learning implementation.
Description for Interpretable Machine Learning Applications
Features of the Course:
Models of Interpretable Machine Learning Acquire the skills to construct and comprehend classification models, such as decision tree and random forest classifiers.
Analysis of Feature Importance Learn to extract and analyze the most significant features affecting model predictions.
Professional Advancement for Machine Learning Developers Acquire understanding of model behavior to improve proficiency as a machine learning developer and modeler.
Advantages for Executives and Consultants Gain expertise in implementing reliable and accountable machine learning solutions, advantageous for decision-makers and consultants.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Coursera Project Network
Duration: 2 hours
Schedule: Hands-on learning
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