Unlocking Explainable AI: Top Free Resources

Learn how to interpret machine learning models using H2O and LIME packages in R. Understand model performance and prediction interpretation.

Hands-on XAI with LIME and H2O in R

 Explore the importance of XAI and its applications, including the Reversed Time Attention Model (RETAIN) and Local Interpretable Model-Agnostic Explanations (LIME).

Introduction to Explainable AI

Decode complex black-box models using local linear surrogates and reason codes for better understanding.

Unveiling Black Box Models

Train ML and deep learning models to predict scenery types in images, grasping concepts like CNNs and residual nets.

AI for Scene Classification

Connect XAI methods with fairness metrics to enhance fairness in AI models.

Explaining Fairness Measures

Develop interpretable ML applications for regression models, decision trees, and random forests.

Interpretable Machine Learning

Increase transparency and accountability in ML models using various techniques like monotonic XGBoost and LIME.

Responsible ML with Python

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