We may earn an affiliate commission when you visit our partners.
Course image
Udacity logo

Secure and Private AI

Andrew Trask

Take Udacity's free Secure AI course and learn the three technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation.

What's inside

Syllabus

In this lesson, you'll learn about the basics of differential privacy, a method for measuring how operations impact the privacy of data.
In this lesson, you'll implement differential privacy in Python.
Read more
Learn how to apply differential privacy to arbitrary algorithms by adding noise to the outputs.
Learn how we can apply differential privacy to deep neural networks.
Learn about federated learning, a method for preserving data privacy by training models where the data lives.
Secure models trained using federated learning with multi-party computation.
Learn how to perform encrypted computation. Build an encrypted database, and generate an encrypted prediction with an encrypted neural network on an encrypted dataset.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Introduces learners to differential privacy and its applications, which are in high demand in modern tech
Covers how to apply differential privacy to deep neural networks, a leading area of research and innovation
Focuses on privacy in machine learning, an urgent and important topic in the field

Save this course

Save Secure and Private AI to your list so you can find it easily later:
Save

Activities

Coming soon We're preparing activities for Secure and Private AI . These are activities you can do either before, during, or after a course.

Career center

Learners who complete Secure and Private AI will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists analyze large amounts of data to reveal trends and patterns, and build models that can make predictions. They use a variety of techniques, including statistical modeling, machine learning, and data mining. The Secure and Private AI course can provide Data Scientists with the skills and knowledge they need to protect sensitive data and build models that are fair and unbiased. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They use a variety of techniques, including data preprocessing, feature engineering, and model training. The Secure and Private AI course can provide Machine Learning Engineers with the skills and knowledge they need to build models that are secure and private. For example, the course covers federated learning, a technique for training models on data that is distributed across multiple devices. This technique can be used to protect data privacy and to improve the accuracy of models.
Privacy Engineer
Privacy Engineers design and implement systems and processes to protect sensitive data. They work with a variety of stakeholders, including data scientists, engineers, and lawyers. The Secure and Private AI course can provide Privacy Engineers with the skills and knowledge they need to protect data privacy in the context of AI. For example, the course covers differential privacy and federated learning, two techniques that can be used to protect data privacy. This course may also help build a foundation for a career in cybersecurity.
Security Engineer
Security Engineers design and implement systems and processes to protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with a variety of stakeholders, including system administrators, network engineers, and application developers. The Secure and Private AI course can provide Security Engineers with the skills and knowledge they need to protect AI systems from attack. For example, the course covers encrypted computation, a technique for protecting data privacy in the cloud. This course may also help build a foundation for a career in cybersecurity.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of stakeholders, including product managers, designers, and end users. The Secure and Private AI course can provide Software Engineers with the skills and knowledge they need to build software systems that are secure and private. For example, the course covers encrypted computation, a technique for protecting data privacy in the cloud. This course may also help build a foundation for a career in cybersecurity.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use a variety of techniques, including statistical analysis, data visualization, and data mining. The Secure and Private AI course can provide Data Analysts with the skills and knowledge they need to protect sensitive data and build models that are fair and unbiased. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Statistician
Statisticians collect, analyze, and interpret data. They work with a variety of stakeholders, including researchers, policymakers, and business leaders. The Secure and Private AI course can provide Statisticians with the skills and knowledge they need to protect sensitive data and build models that are fair and unbiased. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers. The Secure and Private AI course can provide Product Managers with the skills and knowledge they need to develop products that are secure and private. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Business Analyst
Business Analysts identify and solve business problems. They work with a variety of stakeholders, including executives, managers, and end users. The Secure and Private AI course can provide Business Analysts with the skills and knowledge they need to identify and solve business problems related to data privacy and security. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Solutions Architect
Solutions Architects design and implement technical solutions for businesses. They work with a variety of stakeholders, including customers, engineers, and sales teams. The Secure and Private AI course can provide Solutions Architects with the skills and knowledge they need to design and implement solutions that are secure and private. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They work with a variety of stakeholders, including investors, traders, and risk managers. The Secure and Private AI course can provide Quantitative Analysts with the skills and knowledge they need to protect sensitive data and build models that are fair and unbiased. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.
Database Administrator
Database Administrators design, implement, and maintain database systems. They work with a variety of stakeholders, including database users, developers, and system administrators. The Secure and Private AI course can provide Database Administrators with the skills and knowledge they need to protect sensitive data and build systems that are secure and private. For example, the course covers encrypted computation, a technique for protecting data privacy in the cloud.
Cybersecurity Analyst
Cybersecurity Analysts identify, assess, and mitigate cybersecurity risks. They work with a variety of stakeholders, including security engineers, network administrators, and end users. The Secure and Private AI course can provide Cybersecurity Analysts with the skills and knowledge they need to protect AI systems from attack. For example, the course covers encrypted computation, a technique for protecting data privacy in the cloud.
Information Security Analyst
Information Security Analysts design and implement security measures to protect information systems. They work with a variety of stakeholders, including security engineers, network administrators, and end users. The Secure and Private AI course can provide Information Security Analysts with the skills and knowledge they need to protect AI systems from attack. For example, the course covers encrypted computation, a technique for protecting data privacy in the cloud.
Risk Analyst
Risk Analysts identify, assess, and mitigate risks. They work with a variety of stakeholders, including executives, managers, and end users. The Secure and Private AI course can provide Risk Analysts with the skills and knowledge they need to identify and mitigate risks related to data privacy and security. For example, the course covers differential privacy, a technique for protecting data privacy by adding noise to data. This technique can be used to protect data from being re-identified and to prevent attackers from learning sensitive information about individuals.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Secure and Private AI .
Provides a comprehensive overview of machine learning, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of deep learning, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of statistical learning, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of the mathematical foundations of machine learning. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of probabilistic graphical models, from their theoretical foundations to their practical applications. It valuable resource for anyone who wants to learn more about probabilistic graphical models.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable resource for anyone who wants to learn more about information theory, inference, and learning algorithms.
Provides a comprehensive overview of convex optimization, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about convex optimization.
Provides a comprehensive overview of multi-agent systems, from their theoretical foundations to their practical applications. It valuable resource for anyone who wants to learn more about multi-agent systems.
Provides a comprehensive overview of reinforcement learning, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a comprehensive overview of computer vision, from its theoretical foundations to its practical applications. It valuable resource for anyone who wants to learn more about computer vision.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Secure and Private AI .
Generative AI and LLMs on AWS
Gen AI for Data Privacy & Protection
Generative AI Data Privacy and Safe Use for Developers
AI and Gen-AI for Supply Chain Management
Creating Business Value Using Generative AI on AWS
Small Language Models
Security and Privacy for Big Data - Part 2
History of Racial Inequity in Healthcare
Introduction to Hyperledger Self-Sovereign Identity...
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser