We may earn an affiliate commission when you visit our partners.
Google Cloud Training
Learn how to operationalize responsible AI in your organization with this course. Discover best practices and lessons from Google Cloud on building and implementing responsible AI frameworks. Enroll today!
Register for this course and see more details by visiting:
OpenCourser.com/course/t43xk0/responsible
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
UDACITY40
What's inside
Syllabus
If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers industry-standard techniques and tools for responsible AI implementation
Taught by Google Cloud Training, a well-established provider of AI courses
Designed for professionals interested in operationalizing responsible AI in their organizations
Provides practical guidance and lessons learned from Google Cloud on building and implementing responsible AI frameworks
May require prior knowledge or experience in AI and machine learning
Save this course
Save Responsible AI: Applying AI Principles with Google Cloud 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: Applying AI Principles with Google Cloud with these
activities:
Review Python basics
Show steps
Sharpen your Python skills to ensure a solid foundation for this course.
Browse courses on
Python Basics
Show steps
-
Review variables, data types, and operators
-
Practice writing simple Python scripts
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Supplement your theoretical understanding with practical insights from this essential machine learning resource.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read Chapters 1-3 to grasp the basics of machine learning
-
Complete the practice exercises to reinforce your understanding
Join a study group for the course
Show steps
Collaborate with peers to discuss concepts, share insights, and enhance your learning experience.
Show steps
-
Identify fellow course participants for your study group
-
Set regular meeting times and stick to them
-
Prepare discussion topics and ensure active participation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a simple machine learning model using Python
Show steps
Apply your knowledge to create a working model, solidifying your grasp of machine learning concepts.
Show steps
-
Choose a dataset for your model
-
Write a Python script to train and evaluate your model
-
Document your model's performance and findings
Explore the TensorFlow tutorials
Show steps
Follow guided tutorials to expand your knowledge and enhance your practical skills.
Show steps
-
Start with the TensorFlow Basics tutorial
-
Complete the tutorials relevant to your machine learning interests
Attend a workshop on machine learning ethics
Show steps
Deepen your understanding of the ethical implications of machine learning and AI.
Show steps
-
Research and identify relevant workshops
-
Register and attend the workshop
-
Actively participate and engage with the material
Participate in a machine learning hackathon
Show steps
Challenge yourself and test your skills in a competitive environment to accelerate your learning.
Show steps
-
Find a hackathon that aligns with your interests
-
Form a team or work individually
-
Develop and submit your machine learning solution
Review Python basics
Show steps
Sharpen your Python skills to ensure a solid foundation for this course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review variables, data types, and operators
- Practice writing simple Python scripts
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Supplement your theoretical understanding with practical insights from this essential machine learning resource.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to grasp the basics of machine learning
- Complete the practice exercises to reinforce your understanding
Join a study group for the course
Show steps
Collaborate with peers to discuss concepts, share insights, and enhance your learning experience.
Show steps
Show steps
Show steps
- Identify fellow course participants for your study group
- Set regular meeting times and stick to them
- Prepare discussion topics and ensure active participation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a simple machine learning model using Python
Show steps
Apply your knowledge to create a working model, solidifying your grasp of machine learning concepts.
Show steps
Show steps
Show steps
- Choose a dataset for your model
- Write a Python script to train and evaluate your model
- Document your model's performance and findings
Explore the TensorFlow tutorials
Show steps
Follow guided tutorials to expand your knowledge and enhance your practical skills.
Show steps
Show steps
Show steps
- Start with the TensorFlow Basics tutorial
- Complete the tutorials relevant to your machine learning interests
Attend a workshop on machine learning ethics
Show steps
Deepen your understanding of the ethical implications of machine learning and AI.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend the workshop
- Actively participate and engage with the material
Participate in a machine learning hackathon
Show steps
Challenge yourself and test your skills in a competitive environment to accelerate your learning.
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form a team or work individually
- Develop and submit your machine learning solution
Career center
Learners who complete Responsible AI: Applying AI Principles with Google Cloud will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/t43xk0/responsible
Reading list
We've selected eight 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: Applying AI Principles with Google Cloud.
Save
Provides a comprehensive overview of the field of deep learning, covering a wide range of topics from neural networks to reinforcement learning. It valuable resource for anyone who wants to learn more about the latest advances in deep learning.
Explores the potential risks and benefits of artificial intelligence, and argues that we need to develop new ways to align AI with human values.
Save
Provides a comprehensive overview of the safety and security risks associated with the development and use of artificial intelligence. It valuable resource for anyone who wants to learn more about how to mitigate these risks.
Provides a rigorous framework for developing ethical algorithms. It covers a wide range of topics, from fairness to transparency.
Save
Explores the potential risks and benefits of superintelligence. It argues that we need to develop new strategies to ensure that superintelligence is used for good.
Explores the problem of control in artificial intelligence. It argues that we need to develop new ways to ensure that AI systems are aligned with human values.
Save
Explores the potential implications of artificial intelligence for the future of humanity. It argues that we need to develop new ways to live in harmony with AI.
Save
Explores the history and future of machine learning. It argues that machine learning will eventually lead to the development of a master algorithm that will be able to solve any problem.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t43xk0/responsible
Share
Similar courses
Here are nine courses similar to
Responsible AI: Applying AI Principles with Google Cloud.
Responsible AI: Applying AI Principles with Google Cloud
OpenCourser.com/course/iizj1i/responsible
Responsible AI: Applying AI Principles with Google Cloud
Most relevant
AI for Leaders
OpenCourser.com/course/ewey29/ai
AI for Leaders
Ethics & Generative AI (GenAI)
OpenCourser.com/course/sq0d8y/ethics
Ethics & Generative AI (GenAI)
Google Certified Professional Data Engineer
OpenCourser.com/course/gt9zx4/google
Google Certified Professional Data Engineer
Introduction to Generative AI
OpenCourser.com/course/l2hsm1/introduction
Introduction to Generative AI
Responsible AI in the Generative AI Era
OpenCourser.com/course/9qv64z/responsible
Responsible AI in the Generative AI Era
Introducing Generative AI with AWS
OpenCourser.com/course/wdcdy0/introducing
Introducing Generative AI with AWS
AI Governance, Policy, and the Public Good
OpenCourser.com/course/unkc2r/ai
AI Governance, Policy, and the Public Good
AI for Decision Makers
OpenCourser.com/course/vdzf8t/ai
AI for Decision Makers
Effort
1 hour
Via
Udacity
Instructor
Google Cloud Training
Language
English
Good to know
Covers industry-standard techniques and tools for responsible AI implementation
Taught by Google Cloud Training, a well-established provider of AI courses
Designed for professionals interested in operationalizing responsible AI in their organizations
Provides practical guidance and lessons learned from Google Cloud on building and implementing responsible AI frameworks
May require prior knowledge or experience in AI and machine learning
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