Description for Fundamentals of Responsible AI/ML
Comprehending Ethical Risks and Model Bias: Learn about the ethical challenges, societal impacts, and model bias of AI/ML models, as well as the importance of adhering to pertinent regulations.
Problem Identification and Mitigation: Develop the ability to identify and resolve issues such as data drift, correlated features, overfitting, and incorrect inferences in order to guarantee the robustness of AI/ML models.
Frameworks and Explainable AI: Utilize frameworks such as LIME, SHAP, and TCAV to investigate the principles of explainable AI (XAI), with an emphasis on transparency and justifications for a variety of stakeholders.
Regulation and Responsible AI: Comprehend the legal and ethical framework that influences AI/ML models and determine the most effective methods for incorporating responsible practices into the development and deployment of AI.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 4
Offered by: On Udemy provided by Vasco Patricio
Duration: 8h 35m
Schedule: Full lifetime access
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