100 Advanced Level Online Free Courses For Programming Language and Computer Science, Part-6

By Jyoti Nigania |Email | Sep 27, 2018 | 16794 Views

In Part-5 we have covered first 100 courses in number at an intermediate level. As i have classified these courses on the basis of difficulty level. You can find complete lists of the technology-related courses starting later in 2018 on Class Centrals Computer Science, Data Science, and Programming subject pages. I've sorted these courses into the following categories based on their difficulty level.
 Following are the Courses for Advanced Level:

  1. Machine Learning Foundations: A Case StudyApproach from University of Washington
  2. Machine Learning: Regression from University ofWashington
  3. Machine Learning for Data Science and Analyticsfrom Columbia University
  4. Neural Networks for Machine Learning fromUniversity of Toronto
  5. Probabilistic Graphical Models Representation from Stanford University
  6. Intro to Deep Learning from Google
  7. Creative Applications of Deep Learning withTensorFlow
  8. Machine Learning With Big Data from Universityof California, San Diego
  9. Machine Learning for Trading from GeorgiaInstitute of Technology
  10. Hardware Security from University of Maryland,College Park
  11. Bitcoin and Cryptocurrency Technologies fromPrinceton University
  12. Introduction to Artificial Intelligence fromStanford University
  13. Computational Neuroscience from University ofWashington
  14. Reinforcement Learning from Brown University
  15. Machine Learning: Classification fromUniversity of Washington
  16. Intro to Parallel Programming from Nvidia
  17. Advanced Operating Systems from GeorgiaInstitute of Technology
  18. Introduction to Computer Vision from GeorgiaInstitute of Technology
  19. Enabling Technologies for Data Science andAnalytics: The Internet of Things from Columbia University
  20. Interactive 3D Graphics from Autodesk
  21. Machine Learning from Georgia Institute ofTechnology
  22. Applied Cryptography from University ofVirginia
  23. Parallel programming from Ecole PolytechniqueFederale de Lausanne
  24. Introduction to Computer Architecture fromCarnegie Mellon University
  25. Probabilistic Graphical Models 2: Inferencefrom Stanford University
  26. Machine Learning: Clustering and Retrievalfrom University of Washington
  27. Practical Predictive Analytics: Models andMethods from University of Washington
  28. Regression Modeling in Practice from WesleyanUniversity
  29. Quantitative Formal Modeling and Worst-CasePerformance Analysis from EIT Digital
  30. Cryptography II from Stanford University
  31. Nearest Neighbor Collaborative Filtering fromUniversity of Minnesota
  32. Introduction to Operating Systems from GeorgiaInstitute of Technology
  33. High Performance Computer Architecture fromGeorgia Institute of Technology
  34. Computability, Complexity & Algorithms fromGeorgia Institute of Technology
  35. Computational Photography from GeorgiaInstitute of Technology
  36. Artificial Intelligence (AI) from ColumbiaUniversity
  37. Cloud Computing Applications, Part 2: Big Dataand Applications in the Cloud from University of Illinois at Urbana-Champaign
  38. Relational Database Support for Data Warehousesfrom University of Colorado System
  39. Practical Deep Learning For Coders, Part 1 fromfast.ai
  40. Convolutional Neural Networks fromdeeplearning.ai
  41. Introduction to Deep Learning fromMassachusetts Institute of Technology
  42. Deep Learning for Self-Driving Carsfrom Massachusetts Institute of Technology
  43. Computation Structures 3: Computer Organizationfrom Massachusetts Institute of Technology
  44. Applied Machine Learning in Python fromUniversity of Michigan
  45. High Performance Computing from GeorgiaInstitute of Technology
  46. GT Refresher Advanced OS from Georgia Instituteof Technology
  47. Intro to Information Security from GeorgiaInstitute of Technology
  48. Knowledge-Based AI: Cognitive Systems fromGeorgia Institute of Technology
  49. Artificial Intelligence from Georgia Instituteof Technology
  50. Cyber-Physical Systems Security from GeorgiaInstitute of Technology
  51. Network Security from Georgia Institute ofTechnology
  52. Compilers: Theory and Practice from GeorgiaInstitute of Technology
  53. Machine Learning from Georgia Institute ofTechnology
  54. Machine Learning from Georgia Institute ofTechnology
  55. Cyber-Physical Systems Design and Analysisfrom Georgia Institute of Technology
  56. Machine Learning from Columbia University
  57. NP-Complete Problems from University of California, San Diego
  58. Parallel Programming in Java from RiceUniversity
  59. Distributed Programming in Java from RiceUniversity
  60. Concurrent Programming in Java from RiceUniversity
  61. Information Security: Context and Introductionfrom University of London International Programmes
  62. Basic Modeling for Discrete Optimization fromUniversity of Melbourne
  63. Solving Algorithms for Discrete Optimizationfrom University of Melbourne
  64. Advanced Modeling for Discrete Optimization fromUniversity of Melbourne
  65. MATLAB et Octave pour debutants from Ecole Polytechnique Federale de Lausanne
  66. Nature, in Code: Biology in JavaScript from Ecole Polytechnique Federale de Lausanne
  67. Higher School of Economics
  68. Introduction to Deep Learning from HigherSchool of Economics
  69. Natural Language Processing from Higher Schoolof Economics
  70. Bayesian Methods for Machine Learning fromHigher School of Economics
  71. Introduction to Formal Concept Analysis fromHigher School of Economics
  72. Higher School of Economics
  73. Deep Learning in Computer Vision from HigherSchool of Economics
  74. Cloud Computing Management from University System of Maryland
  75. Deploying Applications with Heroku
  76. How to create in Android
  77. The MVC Pattern in Ruby
  78. Developing Android Apps
  79. Learn Backbone.js
  80. VR Scenes and Objects
  81. UIKit Fundamentals
  82. C++ For Programmers
  83. Fundamentals of Red Hat Enterprise Linux from Red Hat
  84. SQL for Data Analysis
  85. Hacker101 from HackerOne
  86. iOS Persistence and Core Data
  87. Fundamentals of Parallelism on Intel Architecture from Intel
  88. Android Basics: Data Storage
  89. iOS Networking with Swift
  90. iOS Design Patterns
  91. Building iOS Interfaces
  92. How to Make an iOS App
  93. VR Design
  94. An Introduction to Practical Deep Learning from Intel
  95. Fundamentals of Containers, Kubernetes, and Red Hat OpenShift from Red Hat
  96. Swift for Developers
  97. VR Platforms & Applications
  98. Dynamic Web Applications with Sinatra
  99. Designing RESTful APIs
  100. Deep Learning Explained from Microsoft

Source: HOB