- https://github.com/trekhleb/homemade-machine-learning There are two folders homemade and notebook, homemade contains all well commented low level code and doc explaining maths. Notebook is like a more of API use.
- https://github.com/Avik-Jain/100-Days-Of-ML-Code It contains more pictorial explanation with just API usage, worth seeing, might come in handy to revise quickly
- https://github.com/rushter/MLAlgorithms This contains fundamental implementation of bit more advanced algorithms.
- https://github.com/topics/machine-learning-algorithms I went there and scrolled, you can do that too
- If this is not helpful, you can search for "implement {x , x=(linear, logistic regression, svm, decision tree)}", follow a few article by hand, if you don't get a feel of how to implement it from the first article.
- If you have done all of the above step, maybe take some random data, try to remember the the theory, and implement it yourself, it will help