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Feature Engineering for BeginnersCodeSignal
AI LuminariesStanford University
Taught byCodeSignalStanford UniversityTier 1: world-leading universities and India's top institutesBest here
Institution score55/10097/100Best here
UniversityNoYes
PlatformedXYouTube
TypeCourseVideo playlist
PriceFree to auditCertificate $30Free videoBest here
CertificatePaid certificateBest hereNo certificate
Learners or views34,852,594Best here
TimeNot stated20 videos, 15 hours
LevelBeginnerNot stated
SyllabusNot listed20 videos
SubjectsData Analysis, Artificial Intelligence, Machine LearningArtificial Intelligence
Before you startBeginner: Data Cleaning and Preprocessing (Numpy, Pandas, Python, Scikit-learn) Beginner: Data Ingestion and Extraction (Numpy, Pandas, Python, Scikit-learn)Not stated
Advantages
  • University courses you can audit for free, with lectures, readings and practice quizzes.
  • A verified certificate from the university if you pay for it.
  • 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
Disadvantages
  • Graded assignments and the certificate need the paid track.
  • Audit access can expire a few weeks after the course ends.
  • Learning is free, but the certificate costs money.
  • Some parts (graded work, certificate) are paid.
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 score55/10093/100Best here
Syllabus side by side
Feature Engineering for Beginners
  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

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