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Introduction to GitHub ActionsDuke University
Generative AI ProgramStanford University
Taught byDuke UniversityTier 2: excellent universities and the companies that build the technologyStanford UniversityTier 1: world-leading universities and India's top institutesBest here
Institution score86/10097/100Best here
PlatformCourseraYouTube
TypeCourseVideo playlist
PriceFree videoBest here
CertificatePaid certificateBest hereNo certificate
Rating3.223 ratingsNo ratings
Learners or viewsNot stated3,181,431
Time2 hoursBest here24 videos, 2 hours
TeachersAlfredo DezaNot stated
Syllabus1 parts24 videosBest here
SubjectsSoftware Engineering, DevOps, Artificial Intelligence, Machine Learning, Git and GitHubArtificial Intelligence, Generative AI
Advantages
  • Structured courses with graded quizzes, assignments and deadlines you can reset.
  • A shareable certificate from the university or company when you pay.
  • Financial aid is often approved for students in India (apply 15 days before you need it).
  • From Duke University, a well-regarded name.
  • Self-paced: start any time.
  • A clear syllabus (1 parts) you can see before you start.
  • Hands-on: you build or practise, not just watch.
  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
  • Taught by Stanford University, one of the strongest names in its field.
  • 3,181,431 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (24 videos) you can see before you start.
Best here
Disadvantages
  • Paid after a 7-day free trial (Coursera Plus or per course).
  • Some courses can be audited for free, but graded work and certificates need payment.
Best here
  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • Graduate level: needs linear algebra, probability and programming first.
  • No certificate.
  • No graded assignments or feedback.
Technifyed score65/10093/100Best here
Syllabus side by side
Introduction to GitHub Actions
  1. Project Overview
Generative AI Program
  1. Overview: Business Opportunities and Applications of Generative AI
  2. Course Overview - Business Opportunities and Applications of Generative AI
  3. These AI Skills Are the Most Valuable
  4. James Landay Explains Why AI Should Be Human-Centered
  5. Stanford Course - Technical Fundamentals of Generative AI
  6. Course Overview - Technical Fundamentals of Generative AI
  7. Stanford Webinar - Human-Centered AI: Designing Systems People Trust
  8. Top 5 Generative AI Trends: James Landay Reacts and Responds
  9. Generative AI: Looking Beyond the Hype with James Landay
  10. Generative AI at a Glance: An Overview from James Landay
  11. Move Forward Faster with Flexible Evals - Stanford Professor Chris Potts #generativeai #evals #ai
  12. Successful Startups Share This Trait - Michelle Pokrass, Post-Training Research Lead at OpenAI
  13. The Importance of Few-Shot Examples - Stanford Professor Chris Potts #generativeai #ai
  14. The Capabilities Overhang - Michelle Pokrass, Post-Training Research Lead at OpenAI #generativeai
  15. Where Might AI Be Underused? - Stanford Professor Chris Potts #generativeai #ai
  16. The State of AI and Trust Among Business Leaders - Aditya Challapally, ML Engineer at Microsoft #ai
  17. Who Decides How Models Behave? - Aditya Challapally, ML Engineer at Microsoft #generativeai
  18. Stanford Webinar - Making GenAI Useful: Lessons from Research and Deployment
  19. Generative AI Program: Technology, Business & Society
  20. Course Overview - Business Opportunities and Applications of Generative AI
  21. Course Overview - Human-Centered Generative AI
  22. Course Overview - Technical Fundamentals of Generative AI
  23. Business Opportunities and Applications of Generative AI
  24. Course Overview - Human-Centered Generative AI

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