# NLP-LOVE/ML-NLP

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

Repository: https://github.com/NLP-LOVE/ML-NLP
Canonical: https://ross.abutalabs.com/products/ml-nlp
Homepage: http://era.dx3906.info
Language: Jupyter Notebook
License Family: other
Topics: nlp, machine-learning, deep-learning
Last push: 2026-01-09T15:16:21+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 61, release rhythm 35, longevity 100
- inputs: {"age_days": 2616, "days_push": 236, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 17799, forks 4625 (observed 2026-08-28T04:11:20.446514+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, data-science
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: interview-preparation, jupyter-notebooks, study-notes, algorithms, chinese-language, natural-language-processing

## Member repositories
- NLP-LOVE/ML-NLP (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:20.446514+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:02:26.928313+00:00, confidence not recorded.
  - readme: https://github.com/NLP-LOVE/ML-NLP (fetched 2026-08-28T04:11:20.446514+00:00, sha d9f1820daf5f)
  - homepage: http://era.dx3906.info (fetched 2026-08-29T08:00:30.973820+00:00, sha e720b92c0aa8)
- Data as of 2026-08-30T08:39:29.467469+00:00.