Interpretable ML Applications: Part 2
The project's primary objective is to enhance the interpretability of machine learning models by facilitating the elucidation of individual predictions using LIME.
Description for Interpretable ML Applications: Part 2
LIME Implementation: Acquire the ability to employ Local Interpretable Model-agnostic Explanations (LIME) as a method for interpreting machine learning predictions.
Individual Prediction Explanation: Develop the capacity to provide a comprehensive explanation of the individual predictions made by trained machine learning models, thereby providing insight into the model's methodology for achieving specific results.
Interpretability in Machine Learning Applications: Implement explainability features in machine learning applications to facilitate more transparent decision-making.
Career Advancement: Improve your professional prospects by acquiring the ability to elucidate and justify the behavior of machine learning models, thereby increasing their credibility and accountability in real-world applications.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Coursera Project Network
Duration: 90-120 minutes at your own pace
Schedule: Hands-on learning
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