NLP-LOVE/ML-NLP resource

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17799 stars · 4625 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated collection of machine learning, deep learning, and NLP interview knowledge points with code implementations, organized into modules covering topics like regression, decision trees, SVM, and probabilistic graphical models. It serves as a theoretical foundation reference for algorithm engineers, primarily written in Chinese with Jupyter Notebook examples.

Use cases

• prepare for machine learning engineer interviews
• review NLP interview questions and answers
• study classic ML algorithms with code examples
• refresh deep learning fundamentals before an interview
• learn how XGBoost, GBDT, and LightGBM work
• find a structured ML/NLP knowledge roadmap

When to choose

• you are preparing for ML/NLP algorithm engineer interviews
• you want concise theory summaries paired with runnable code
• you prefer Chinese-language study materials
• you need a quick refresher on classic algorithms like SVM, decision trees, or EM

When to avoid

• you need production-ready ML libraries or frameworks
• you require comprehensive textbook-depth coverage of every topic
• you only read English-language documentation
• you need a formally licensed resource for redistribution

Facets

learning-resource · maturity active

machine-learning deep-learning nlp data-science machine-learning deep-learning tutorials education python cross-platform interview-preparation jupyter-notebooks study-notes algorithms chinese-language natural-language-processing

Member repositories

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