In case you missed them, here is a thread (in no special order) of some machine learning guides and lists of recommendations I have carefully put together over the last few weeks.
I hope they are useful. Feedback is always appreciated.
Happy Learning!
[Thread]
I hope they are useful. Feedback is always appreciated.
Happy Learning!

[Thread]

I strongly believe that applied ML research is just as important as theoretical research. Learn ways how to get started with applied ML research.
https://elvissaravia.substack.com/p/getting-started-with-applied-ml-research

Maths is fundamental to learning and building deeper intuitions about machine learning methods. This article includes my recommendations on resources where you can get started.
https://elvissaravia.substack.com/p/my-recommendations-to-learn-mathematics

You may feel tempted to jump directly into building and training models in 10 lines of code. However, in doing so, you miss out on an opportunity to learn the fundamental of ML.
https://elvissaravia.substack.com/p/course-recommendations-for-introductory

Machine learning is no longer something only academics are curious to apply. Companies all over the world are powering their products using ML. Learn about ML in production using these resources:
https://elvissaravia.substack.com/p/my-recommendations-to-learn-machine

As a data scientist, it's very important to learn how to analyze textual content and build machine learning models that work for text use cases. Learn NLP following some of these resources:
https://elvissaravia.substack.com/p/my-recommendations-for-getting-started

You have some knowledge of ML and want to build awesome machine learning projects that stand out. I have summarized a few tips on how to build awesome machine learning projects in this guide:
https://github.com/dair-ai/awesome-ML-projects-guide