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

Note : This is a rough roadmap to guide you on your Machine Learning journey. We recommend everyone to focus on core CS principles and industry-standard tech stacks with a strong focus on Machine Learning theory and development. This might look overwhelming at first, but it's really not. 🙂

Roadmap

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

• [The Missing Semester from your CS education] (https://missing.csail.mit.edu/)
• Basic computer architecture:
  • GPU vs CPU
  • File Systems
• Linux
• Containers - Docker
• Bash Cheatsheet
• Git-Introduction

• 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

• List of active projects

• 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 - 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 skills ---> 😎

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

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

• Full Stack Deep Learning
• [Full Stack Python] (https://www.fullstackpython.com/)
• TensorFlow: Data and Deployment Specialization
• Django
• Flask
• Flutter

• Deep Learning (with Pytorch)
  • DS-GA 1008: Deep Learning | SPRING 2020
• Introduction to Deep Learning
  • MIT 6.S191: Introduction to Deep Learning | 2020
• CNNs for Visual Recognition
  • CS231n: CNNs for Visual Recognition, Stanford | Spring 2019
• NLP with Deep Learning
  • CS224n: NLP with Deep Learning, Stanford | Winter 2019
• Deep Reinforcement Learning
  • CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2020
  • CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2019
• Unsupervised Learning
  • CS294-158-SP20: Deep Unsupervised Learning, UC Berkeley | Spring 2020
• Multi-Task and Meta Learning
  • Stanford CS330: Multi-Task and Meta-Learning | 2019

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

Note : Learn from official tutorials/docs or GitHub repos which have detailed notebooks like Grokking Deep Learning or PyTorch Examples

• Neural Networks and Deep Learning
• Deep Learning Book

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

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

• Reinforcement Learning – An Introduction
• Spinning Up RL (Blog)

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

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

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