Webinar: Interpretable Machine learning

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As machine learning models become increasingly complex, ensuring their transparency and trustworthiness has never been more critical. This webinar explores the field of Interpretable Machine Learning, showcasing how tools like SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), can make black-box models more understandable. Learn how these techniques can explain predictions, uncover biases, and build trust in AI systems across industries. A live demonstration will highlight practical applications, giving you the knowledge to integrate interpretability into your own ML workflows.

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