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Build Responsible AI Systems with Google. Learn how to design and build fair, transparent, secure, and safe AI systems
Instructor: Google Cloud Training
Included with
(9 reviews)
Recommended experience
Intermediate level
AI/ML developers, AI practitioners, ML engineers, and data scientists
(9 reviews)
Recommended experience
Intermediate level
AI/ML developers, AI practitioners, ML engineers, and data scientists
Learn how to identify and mitigate bias in AI systems.
Learn how to understand the behavior of Machine Learning models using various interpretability techniques.
Learn about privacy considerations in AI projects and how to build secure AI systems.
Learn how to safely use Generative AI models and fine-tune for better alignment.
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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
Define what is Responsible AI
Identify Google’s AI principles
Describe what AI fairness and bias mean
Explain how to identify and mitigate biases through data and modeling
Define interpretability and transparency as it relates to AI
Describe the importance of interpretability and transparency in AI
Explore the tools and techniques used to achieve interpretability and transparency in AI
Define what AI privacy and AI safety is.
Describe methods used to address AI privacy in both data and models.
List key considerations for AI safety implementation.
Describe techniques used when implementing AI safety.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
This accelerated specialization is designed to be completed in only two weeks.
Technical understanding about Machine Learning / Neural Networks and experience of building AI systems are helpful in understanding the concepts covered.
We strongly recommend you take these courses in order, beginning with Responsible AI for Developers: Fairness & Bias, which provides an overview of Responsible AI at Google.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
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