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Introduction to Agentic AIIBM
AI LuminariesStanford University
Generative AI ProgramStanford University
Taught byIBMTier 2: excellent universities and the companies that build the technologyStanford UniversityTier 1: world-leading universities and India's top institutesBest hereStanford UniversityTier 1: world-leading universities and India's top institutesBest here
Institution score84/10097/100Best here97/100Best here
UniversityNoYesYes
PlatformCognitive ClassYouTubeYouTube
TypeCourseVideo playlistVideo playlist
PriceFree courseFree videoFree video
CertificateFree badgeBest hereNo certificateNo certificate
RatingNo ratingsNo ratingsNo ratings
Learners or views110,0004,852,594Best here3,181,431
Time3 hours20 videos, 15 hours24 videos, 2 hoursBest here
PaceSelf-pacedSelf-pacedSelf-paced
LevelBeginnerNot statedNot stated
LanguageEnglishEnglishEnglish
SubtitlesNot statedNot statedNot stated
StartsAny timeAny timeAny time
TeachersNot statedNot statedNot stated
SyllabusNot listed20 videos24 videosBest here
Skills only this one coversAgentic AIAI AgentArtificial IntelligenceNoneNone
SubjectsArtificial Intelligence, Generative AI, AI AgentsArtificial IntelligenceArtificial Intelligence, Generative AI
Before you startNot statedNot statedNot stated
Advantages
  • Free, with free IBM digital badges on Credly.
  • Browser labs, so nothing to install.
  • From IBM, a well-regarded name.
  • 110,000 learners have taken it, so help and notes are easy to find.
  • Free digital badge you can add to LinkedIn.
  • Completely free.
  • Self-paced: start any time.
  • 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.
  • 4,852,594 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (20 videos) you can see before you start.
Best here
  • 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
  • Some courses are older and not refreshed.
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.
  • 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 score85/10093/100Best here93/100Best here
Syllabus side by side
Introduction to Agentic AI
  1. Not listed
AI Luminaries
  1. Stanford Lecture - Strong Components and Weak Components, Dr. Donald Knuth I 2024
  2. Stanford Webinar - How AI is Changing Coding and Education, Andrew Ng & Mehran Sahami
  3. Ask About AI: Professor Chris Manning Answers AI-Generated Questions
  4. Ask About AI: Professor Chris Manning Answers Your AI Career Questions
  5. Andrew Ng: Opportunities in AI - 2023
  6. Andrew Ng and Chris Manning Discuss Natural Language Processing
  7. AI and Robotics - Chelsea Finn & Andrew Ng
  8. What is our ethical responsibility with AI & Machine Learning? - Fei-Fei Li & Andrew Ng
  9. Andrew Ng and Fei-Fei Li Discuss Human-Centered Artificial Intelligence - Stanford Online
  10. What advice do you have for getting started in AI & Machine Learning? - Fei-Fei Li & Andrew Ng
  11. Is a Career in AI and Machine Learning Right for Me? - Fei-Fei Li & Andrew Ng
  12. Stanford CS25: V2 I Represent part-whole hierarchies in a neural network, Geoff Hinton
  13. Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)
  14. Stanford Lecture: Don Knuth - "Pi and The Art of Computer Programming" (2019)
  15. Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
  16. Stanford Lecture: TeX For Beginners - Session 1 (Donald Ervin Knuth on February 23, 1981)
  17. Live Office Hours with Yuri Sagalov and Sam Altman - Stanford CS183F: Startup School
  18. How and Why to Start A Startup - Sam Altman & Dustin Moskovitz - Stanford CS183F: Startup School
  19. Stanford Seminar - Can the brain do back-propagation? Geoffrey Hinton
  20. Jen-Hsun Huang: Stanford student and Entrepreneur, co-founder and CEO of NVIDIA
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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