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Here, I list the resources that I read near the end of my PhD to help prepare with getting a job outside of academia.
- I read “An Introduction to Statistical Learning” to get a broad knowledge about the machine learning algorithms out there.
- For optimization, these lectures and its corresponding notes are useful.
- For time series (data) analysis, these series of lectures and its accompanying notes are helpful.
- For deep generative models, these graduate-level lectures are helpful.
- For large language models (LLM), this tutorial is useful.
- This book (and its answers) helped to explain how machine learning interview works.
- For a quick recap, I read through this data science cheatsheet (alternatively, this other cheatsheet is more comprehensive).
- For deeplearning, I read this online book.
- For SQL, this free course with its useful cheatsheets (basics, tables, intermediate, functions, accessing with Python) is helpful.
- For data structure and algorithms reference and cheatsheet, I looked at these two sources: (i) and (ii).
- A cheatsheet for Python can be found here.