This repository provides structured knowledge points and code implementations across machine learning, deep learning, and NLP domains. It is organized by topic modules including linear regression, logistic regression, decision trees, random forests, and gradient boosting, with practical code examples at the end of each chapter. The project serves as a comprehensive review resource for algorithm engineers preparing for technical interviews in the ML and NLP space.