I think it's a bit far fetched to assume that recruiters tailor interview questions based on these Github repo's. For me as a junior data scientist it really useful to test my own knowledge and highlight areas which I need to study more.
This probably won't be the case for a question as basic as, "what is regression?" But for any intermediate to advanced interview question involving regression, I would expect companies to jealously guard it.
If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.
A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home
Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/
There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learnin...
If the goals is to have a skill hire, typically someone who can maintain an existing, well-documented system, then having them know trivial details and banked information can be quite helpful. On the other hand, talent hires I would take in a different direction. If the candidate's only stand out quality is a clear memorization of banked answers I would wonder whether they could work from fundamentals.