An AI engineer Prepares / 算法工程师自我修养

1. NTU_Probability-I-II(**大学，叶丙成， 顽想学概率一，二)

2. MachineLearing-Hsuan-Tien-Lin（机器学习基石&技法，**大学，林轩田）

3. Probabilistic Graphical Models, Stanford University

5. CMU_PGM_Eric Xing, Probabilistic Graphical Models

6. EE364

7. Machine Learning, Andrew Ng

8. CS229

9. CS231n

10. Deep-Learning-Specialization, Andrew Ng

11. Mathematics for Machine Learning

12. Python for Everybody

14. CS294-112

17. Hands-on Introduction to Linux Commands and Shell Scripting

18. Advanced Machine Learning 专项课程

19. Programming in C++: A Hands-on Introduction 专项课程

21. Applied Data Science with Python 专项课程

22. 数字信号处理

24. Learn English: Intermediate Grammar

25. Fundamentals of Digital Image and Video Processing

26. Machine Learning

27. Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital

28. TensorFlow in Practice

29. AI For Everyone

30. Julia Scientific Programming

31. GPU Programming 专项课程

32. Computer Vision Specialization

33. Discrete Optimization

34. Fundamentals of Reinforcement Learning

35. CS224W: Machine Learning with Graphs

36. Generative Adversarial Networks (GANs)