McCarthy-AI-Roadmap

Roadmap to learn AI for associates at McCarthy Lab@Next Tech Lab

Original repository : https://github.com/niladridutt/McCarthy-AI-Roadmap

Dependency Tree

Primer

1. Data Science Stack

• Best places to learn -
  • https://www.python-course.eu/
  • https://www.datacamp.com/
  • https://www.pythonprogramming.net/
• Python
• NumPy
• Pandas
• Matplotlib and Seaborn
• scikit-learn
• SciPy

2. Technology Stack

• Basic computer architecture:
  • GPU vs CPU
  • File Systems
• Linux
• Containers - Docker
• Bash Cheatsheet
• Git-Introduction

3. Deep Learning Frameworks

• PyTorch
  • Deep Learning with PyTorch - excellent resource for learning
  • Use fast.ai as High level wrapper (not recommended due to instability of the library and lack of adequate documentation)
• TensorFlow
  • Use tf.keras as a High level wrapper
  • Effective TensorFlow

4.Reinforcement Learning - Libraries

• TensorLayer
• Keras-RL

Mooc Resources

Data Structures and Algorithms

• Stanford ALgorithms - Coursera or
• Introduction to Algorithms (MIT 6.006)
• Introduction to Computational Thinking and Data Science (MIT 6.0002)
• Learn any one programming language really well and compete on Codechef, Hackerrank, HackerEarth,etc

Machine Learning/AI MOOCs

• Machine Learning - Coursera
• UC Berkeley CS188 or
• MIT 6.034

Note :

• Implement Machine Learning models from scratch using Python
• Once you're comfortable implementing models from scratch, learn scikit-learn and compare performance
• Practice on Kaggle to get your skiills ---> 😎

Deep Learning MOOCs

• TensorFlow in Practice Specialization - Coursera
• fast.ai
• Stanford University's CS224n - NLP

Reinforcement Learning Tutorials

• David Silver
• Practical Reinforcement Learning
• Practial RL - Yandex Data School

Deploying/Shipping Projects:

Feel free to use any of these frameworks, all are not required

• Full Stack Deep Learning
• TensorFlow: Data and Deployment Specialization
• Django
• Flask
• Flutter

Books and Further reading material

Machine Learning Books for reference

• Introduction to Statstical Learning
• Elements of Statistical Learning (A little more in-depth than ISLR)
• Pattern Recognition And Machine Learning

Note : Learn from official tutorials/docs or GitHub repos which have detailed notebooks like Hvass Labs

Deep Learning Books

• Deep Learning Book

Mathematics for Machine Learning

• The Matrix Calculus You Need For Deep Learning - - Quick refresher
• Mathematics for Machine Learning - Intermediate
• Numerical Algorithms - Advanced

Natural Language Processing

• Natural Language Processing by National Research University Higher School of Economics
• NLP course by Yandex Data School

Reinforcement Learning Books

• Reinforcement Learning – An Introduction

Read blogs, Reddit, follow researchers on Twitter

• Towards Data Science
• Sebastian Ruder
• montreal.ai
• thegradient
• Reddit - Machine Learning
• Reddit - Deep Learning
• https://github.com/ujjwalkarn/Machine-Learning-Tutorials

Podcasts to Follow Interesting Developments In The Field

• TWIML AI Podcast
• The Data Skeptic
• The AI Podcast - Nvidia
• Artificial Intelligence with Lex Fridman, MIT AI
• Linear Digressions

Feel free to make Pull Requests stating why that particular resource should be added.