ML from Scratch for Interview

In preparing for technical interviews, I've frequently come across the task of coding basic algorithms from the ground up. While there's no shortage of resources on this topic, I've noticed a common issue: some explanations are excessively complex, making it unrealistic to reproduce such detailed solutions within a typical 30-minute interview timeframe. Others oversimplify the matter, failing to engage the problem-solving skills expected of a software engineer. I firmly believe that mastery of both machine learning and fundamental data structures and algorithms is crucial for a machine learning engineer. Bridging the gap between theoretical concepts taught in algorithm classes and their practical application in machine learning models is not only intellectually stimulating but also immensely satisfying. This repository is my attempt to strike a balance, presenting algorithmic challenges at a level of complexity that is both thought-provoking and manageable within the constraints of an interview setting.