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Introduction to GitHub ActionsDuke University
AI LuminariesStanford 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 stated4,852,594
Time2 hoursBest here20 videos, 15 hours
TeachersAlfredo DezaNot stated
Syllabus1 parts20 videosBest here
SubjectsSoftware Engineering, DevOps, Artificial Intelligence, Machine Learning, Git and GitHubArtificial Intelligence
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.
  • 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
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
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

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