anandrajneesh/path1.md

## path1.md

      
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              path1.md
            
          
    Python
MIT 6.042 - Mathematics for Computer Science

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/index.htm
Andrew Ng’s course on ML
Peter Norvig’s book on AI
Stanford’s CS231n - ConvNets for Visual Recognition. Go for the 2016 version
http://cs231n.stanford.edu/
good library(Tensorflow/Torch), a machine with a GPU, and start implementing pet projects, such as classifiers and generators

  
## path2.md

      
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              path2.md
            
          
    Maths :

Linear algebra

Probability theory

Optimization

Calculus

Information theory and decision theory

Matrix analysis
Python
Books:

https://web.stanford.edu/~hastie/Papers/ESLII.pdf

http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf

http://www.deeplearningbook.org/
Andrew Ng Course
Practice : Kaggle.com

  
## path3.md

      
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              path3.md
            
          
    https://medium.com/machine-learning-world/learning-path-for-machine-learning-engineer-a7d5dc9de4a4

  
## path4.md

      
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              path4.md
            
          
    https://in.udacity.com/course/deep-learning--ud730-india

https://www.deeplearning.ai/

http://course.fast.ai/index.html