build-your-own-x-machine-learning

Why we included this project

Most machine learning courses hand you a library and tell you to call fit(). This collection goes the other way: it walks through implementing the core algorithms yourself in Python, from linear and logistic regression through decision trees, SVMs, and clustering, and on to neural networks, deep learning, and large language models. Each tutorial links to a self-contained script you can read, run, and modify, which makes it handy when you are preparing for interviews, teaching a class, or trying to see what actually happens inside a framework call. The coverage also stretches into recommendation systems, computer vision, NLP, time series, and anomaly detection, so it works as a structured roadmap for filling gaps in your fundamentals. It is a curated index of tutorials rather than a library, so plan to learn from the code and explanations instead of dropping it into a production stack.