Zero to Hero With 250 FREE Online Programming and Computer Science Courses from Top Universities Part 3.24 min read

I’ve been sharing with you 5 parts(1.1, 1.2, 2.1, 2.2, 3.1) of this series about free online programming and computer science courses. ‘216 courses’ from the last few parts that contains a lot of practical things. But it’s not the end! Now I am happy to share with you last 44 courses as well as the last part of these series articles. I hope you enjoy it!

250 FREE ONLINE PROGRAMMING & COMPUTER SCIENCE COURSES FROM TOP UNIVERSITIES PART 3.2

Six years ago, universities like MIT and Stanford first opened up free online courses to the public. Today, more than 800 schools around the world have created thousands of free online courses.

You can also go to Class Central’s homepage to find many free courses available up to 10000 from top universities.

                                           Class Central’s home page.

I’ve sorted these courses into the following categories based on their difficulty level as well as 3 parts of this topic:

  • Beginner
  • Intermediate
  • Advanced

The first sections will be for beginners(part 1.1, part 1.2), the second will be for those who are looking for intermediate courses(part 2.1, part 2.2) and the rest will be for advanced courses(part 3.1, part 3.2). All of these are aggregated in 250 free courses.

In this part I will introduce you to the courses that are suitable for advanced learners, if you are looking for beginner courses click here, intermediate courses click here. 

ADVANCED PROGRAMMING COURSES PART 3.2 (44)

  1. MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne
  2. Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne
  3. Менеджмент информационной безопасности from Higher School of Economics
  4. Методы и средства защиты информации from Higher School of Economics
  5. Обработка изображений from Higher School of Economics
  6. Introduction to Formal Concept Analysis from Higher School of Economics
  7. Practical Reinforcement Learning from Higher School of Economics
  8. Addressing Large Hadron Collider Challenges by Machine Learning from Higher School of Economics
  9. Matrix Factorization and Advanced Techniques from University of Minnesota
  10. 機器學習基石下 (Machine Learning Foundations) — -Algorithmic Foundations from National Taiwan University
  11. 人工智慧:搜尋方法與邏輯推論 (Artificial Intelligence — Search & Logic)from National Taiwan University
  12. System Validation: Automata and behavioural equivalences from EIT Digital
  13. System Validation (3): Requirements by modal formulas from EIT Digital
  14. Embedded Hardware and Operating Systems from EIT Digital
  15. System Validation (4): Modelling Software, Protocols, and other behaviour from EIT Digital
  16. Learn TensorFlow and deep learning, without a Ph.D. from Google
  17. Machine Learning Crash Course with TensorFlow APIs from Google
  18. Infrastructure as Code from Microsoft
  19. Deep Learning Explained from Microsoft
  20. Introduction to Artificial Intelligence (AI) from Microsoft
  21. DevOps Testing from Microsoft
  22. DevOps for Databases from Microsoft
  23. DevOps Practices and Principles from Microsoft
  24. Advanced C++ from Microsoft
  25. Sparse Representations in Image Processing: From Theory to Practice from Technion — Israel Institute of Technology
  26. Sparse Representations in Signal and Image Processing: Fundamentalsfrom Technion — Israel Institute of Technology
  27. Cyber-Physical Systems: Modeling and Simulation from University of California, Santa Cruz
  28. Statistical Machine Learning from Carnegie Mellon University
  29. Introduction to OpenStack from Linux Foundation
  30. Blockchain for Business — An Introduction to Hyperledger Technologiesfrom Linux Foundation
  31. Introduction to Cloud Foundry and Cloud Native Software Architecturefrom Linux Foundation
  32. Approximation Algorithms Part II from École normale supérieure
  33. Mathematics for Machine Learning: Linear Algebra from Imperial College London
  34. Mathematics for Machine Learning: Multivariate Calculus from Imperial College London
  35. Reliable Distributed Algorithms, Part 2 from KTH Royal Institute of Technology
  36. Mathematics for Machine Learning: PCA from Imperial College London
  37. Computer System Design: Advanced Concepts of Modern Microprocessorsfrom Chalmers University of Technology
  38. Deep Learning for Natural Language Processing from University of Oxford
  39. Cutting Edge Deep Learning For Coders, Part 2 from fast.ai
  40. Cloud Computing Security from University System of Maryland
  41. Continuous Integration and Deployment
  42. Deep Learning Summer School
  43. Access Controls from (ISC)²
  44. Networks and Communications Security from (ISC)²

YOU CAN REFER TO PART 3.1 HERE.

Source: Dhawal Shah on http://freecodecamp.medium.org, you can visit the original article here.
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