This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety.
Throughout the courses, you will learn how to:
Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues.
Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools.
Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust.
Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models.
By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
Applied Learning Project
Throughout the courses, you will perform hands-on projects, including :
Bias mitigation using the TensorFlow Model Remediation library
Explainable AI techniques with Google Cloud Vertex AI
Privacy-preserving machine learning training with DP-SGD
Safeguarding Generative AI systems with Gemini
