ML-NLP

Why we included this project

A structured study companion for anyone prepping for machine learning, deep learning, and NLP interviews, or for engineers who want to keep their theoretical fundamentals sharp. It works through the classic algorithm families, from linear and logistic regression to decision trees, boosting methods, SVM, Bayesian networks, clustering, and EM, then moves into neural networks, CNN/RNN/LSTM, transfer and reinforcement learning, and the NLP stack from word embeddings through seq2seq, attention, Transformer, BERT, and XLNet. Each chapter frames the material as the kind of question an interviewer might actually ask and closes with runnable code examples, so the theory stays grounded in practice. The content is organized by module into a clear knowledge map, which makes it easy to read top to bottom or jump straight to a weak spot before an interview. It is a curated reference rather than a deployable tool, but that is exactly its value: a single, structured place to review the breadth of topics an algorithm engineer is expected to know.