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McCarthy-AI-Roadmap

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

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1. Data Science Stack

2. Technology Stack

3. Deep Learning Frameworks

4. Reinforcement Learning - Libraries

Mooc Resources

Data Structures and Algorithms

Machine Learning/AI MOOCs

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 ---> 😎

Deep Learning MOOCs

Reinforcement Learning Tutorials

Deploying/Shipping Projects

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

Academic Courses

Books and Further reading material

Machine Learning Books for reference

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

Deep Learning Books

Mathematics for Machine Learning

Natural Language Processing

Reinforcement Learning Books

Read blogs, Reddit, follow researchers on Twitter

Podcasts to Follow Interesting Developments In The Field

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

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