We may earn an affiliate commission when you visit our partners.
Google Cloud Training
This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
Register for this course and see more details by visiting:
OpenCourser.com/course/edd49q/responsible
What's inside
Syllabus
Course Introduction
This module introduces the course structure and objectives.
AI Interpretability & Transparency
This module focuses on AI interpretability and transparency. It provides various techniques and tools to help achieve interpretability and transparency in both data and AI models.
Read more
Syllabus
Course Introduction
This module introduces the course structure and objectives.
AI Interpretability & Transparency
This module focuses on AI interpretability and transparency. It provides various techniques and tools to help achieve interpretability and transparency in both data and AI models.
Read more
Course Summary
This module provides a summary of the entire course by covering the most important concepts, tools, and technologies.
Course Resources
Student PDF links to all modules
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides tools and techniques to achieve interpretability and transparency in AI models, a valuable skill for developers and engineers in the field
Taught by Google Cloud Training, recognized for their expertise in AI and cloud computing
Explores practical methods and tools to implement interpretability and transparency, enhancing the reliability and trustworthiness of AI models
Applicable to learners with a background in AI and machine learning who seek to deepen their understanding of AI interpretability and transparency
Enhances learners' ability to develop and deploy AI models that are more interpretable and transparent, increasing their adoption and impact
May require learners to have some prior knowledge or experience in AI and machine learning concepts
Save this course
Save Responsible AI for Developers: Interpretability & Transparency to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Responsible AI for Developers: Interpretability & Transparency with these
activities:
Review AI Interpretability and Transparency
Show steps
Gain a comprehensive understanding of the concepts and techniques discussed in the course.
View
Interpretable Machine Learning
on Amazon
Show steps
-
Read the book's introduction and first chapter.
-
Identify the key concepts and techniques related to AI interpretability and transparency.
Practice Interpreting AI Models
Show steps
Develop practical skills in interpreting and explaining AI models.
Show steps
-
Select a simple AI model (e.g., linear regression, decision tree).
-
Train the model on a dataset.
-
Interpret the model's predictions using techniques learned in the course.
Develop an Infographic on AI Interpretability
Show steps
Enhance visual understanding and communication of AI interpretability concepts.
Show steps
-
Gather information from the course materials and external sources.
-
Design the infographic using visualization tools.
Show all three activities
Review AI Interpretability and Transparency
Show steps
Gain a comprehensive understanding of the concepts and techniques discussed in the course.
View
Interpretable Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter.
- Identify the key concepts and techniques related to AI interpretability and transparency.
Practice Interpreting AI Models
Show steps
Develop practical skills in interpreting and explaining AI models.
Show steps
Show steps
Show steps
- Select a simple AI model (e.g., linear regression, decision tree).
- Train the model on a dataset.
- Interpret the model's predictions using techniques learned in the course.
Develop an Infographic on AI Interpretability
Show steps
Enhance visual understanding and communication of AI interpretability concepts.
Show steps
Show steps
Show steps
- Gather information from the course materials and external sources.
- Design the infographic using visualization tools.
Show all three activities
Career center
Learners who complete Responsible AI for Developers: Interpretability & Transparency will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course may be useful for Machine Learning Engineers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
AI Engineer
AI Engineer
AI Engineers design, develop, and deploy AI systems. This course may be useful for AI Engineers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Data Scientist
Data Scientist
Data Scientists analyze data to extract meaningful insights and help organizations make data-driven decisions. This course may be useful for Data Scientists as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. This course may be useful for Data Analysts who are working on AI projects, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who are working on AI projects, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Project Manager
Project Manager
Project Managers plan and execute projects. This course may be useful for Project Managers who are working on AI projects, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who are working on AI products, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. This course may be useful for Business Analysts who are working on AI projects, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
AI Manager
AI Manager
AI Managers lead teams of AI Engineers. This course may be useful for AI Managers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. This course may be useful for Technical Writers who are working on AI products, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
User Experience Designer
User Experience Designer
User Experience Designers design the user interface for software and hardware products. This course may be useful for User Experience Designers who are working on AI products, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of Data Scientists. This course may be useful for Data Science Managers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Product Marketing Manager
Product Marketing Manager
Product Marketing Managers are responsible for the marketing of new products. This course may be useful for Product Marketing Managers who are working on AI products, as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Machine Learning Manager
Machine Learning Manager
Machine Learning Managers lead teams of Machine Learning Engineers. This course may be useful for Machine Learning Managers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
Data Engineering Manager
Data Engineering Manager
Data Engineering Managers lead teams of Data Engineers. This course may be useful for Data Engineering Managers as it provides a foundation in AI interpretability and transparency, which are important for ensuring that AI models are fair, unbiased, and trustworthy.
For more career information including salaries, visit:
OpenCourser.com/course/edd49q/responsible
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
Responsible AI for Developers: Interpretability & Transparency.
Introduces interpretable machine learning, a subfield of machine learning that aims to make black box models explainable. It covers various techniques and tools to help practitioners understand and interpret ML models.
Provides a comprehensive overview of machine learning, covering the theoretical foundations, practical techniques, and current applications. It is an excellent resource for researchers and practitioners who want to understand and develop machine learning models.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, practical techniques, and current applications. It is an excellent resource for researchers and practitioners who want to understand and develop deep learning models.
Explores the ethical implications of artificial intelligence, and provides a framework for developing AI systems that are fair, accountable, and transparent. It is an excellent resource for anyone who is interested in the ethical development and use of AI.
Provides a practical guide to natural language processing with PyTorch, covering the core concepts, techniques, and tools. It is an excellent resource for developers and engineers who want to build and deploy natural language processing applications.
Provides a comprehensive overview of machine learning algorithms, with a focus on the practical aspects of algorithm design and implementation. It is an excellent resource for developers and engineers who want to understand and build machine learning models.
Save
Provides a broad overview of the safety and security challenges posed by artificial intelligence. It discusses the potential risks and benefits of AI, and explores the ethical and policy implications of developing and deploying AI systems.
Provides a gentle introduction to data science, with a focus on hands-on coding. It good starting point for beginners who want to learn the basics of data science.
Provides a comprehensive overview of statistical learning, including both theory and practice. It valuable resource for advanced learners who want to deepen their understanding of ML.
Provides a comprehensive overview of reinforcement learning, including both theory and practice. It valuable resource for advanced learners who want to deepen their understanding of ML.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/edd49q/responsible
Share
Similar courses
Here are nine courses similar to
Responsible AI for Developers: Interpretability & Transparency.
Ethics in AI and Data Science
OpenCourser.com/course/ahg2g4/ethics
Ethics in AI and Data Science
User Awareness and Education for Generative AI
OpenCourser.com/course/q43zir/user
User Awareness and Education for Generative AI
Principles of Data Science Ethics
OpenCourser.com/course/ba6agk/principles
Principles of Data Science Ethics
Responsible Artificial Intelligence Practices
OpenCourser.com/course/r7ei2b/responsible
Responsible Artificial Intelligence Practices
Generative AI: Impact, Considerations, and Ethical Issues
OpenCourser.com/course/07wdbf/generative
Generative AI: Impact, Considerations, and Ethical Issues
The Data Sessions: ChatGPT Roundtable Discussion with Pluralsight Experts
OpenCourser.com/course/9zhbnl/the
The Data Sessions: ChatGPT Roundtable Discussion with...
Generative AI: Governance, Policy, and Emerging Regulation
OpenCourser.com/course/qv8t8g/generative
Generative AI: Governance, Policy, and Emerging Regulation
Impact, Ethics, and Issues with Generative AI
OpenCourser.com/course/5x6d8a/impact
Impact, Ethics, and Issues with Generative AI
Explainable Machine Learning with LIME and H2O in R
OpenCourser.com/course/948y8z/explainable
Explainable Machine Learning with LIME and H2O in R
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Provides tools and techniques to achieve interpretability and transparency in AI models, a valuable skill for developers and engineers in the field
Taught by Google Cloud Training, recognized for their expertise in AI and cloud computing
Explores practical methods and tools to implement interpretability and transparency, enhancing the reliability and trustworthiness of AI models
Applicable to learners with a background in AI and machine learning who seek to deepen their understanding of AI interpretability and transparency
Enhances learners' ability to develop and deploy AI models that are more interpretable and transparent, increasing their adoption and impact
May require learners to have some prior knowledge or experience in AI and machine learning concepts
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