# apachecn/ailearning

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

Repository: https://github.com/apachecn/ailearning
Canonical: https://ross.abutalabs.com/products/ailearning
Homepage: http://ailearning.apachecn.org/
Language: Python
License: NOASSERTION
License Family: other
Topics: fp-growth, apriori, mahchine-leaning, naivebayes, svm, adaboost, kmeans, svd, pca, logistic, regression, recommendedsystem, sklearn, scikit-learn, nlp, deeplearning, python, dnn, lstm, rnn
Last push: 2024-11-12T16:21:55+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3476, "days_push": 659, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 42490, forks 11514 (observed 2026-08-28T04:12:08.792656+00:00)

## What it is
A Chinese-language open-source learning resource covering data analysis, machine learning, linear algebra, and deep learning, based on notes from 'Machine Learning in Action' plus PyTorch, NLTK, and TensorFlow 2 material. It provides structured docs, example code, datasets, and video links maintained by the ApacheCN community.

## Use cases
- learn machine learning from scratch in Chinese
- study classic ML algorithms like SVM, KNN, and decision trees with code
- find worked examples for k-means, Apriori, and FP-growth
- get started with deep learning using PyTorch and TensorFlow 2
- prepare for machine learning interviews
- follow a structured AI learning roadmap

## When to choose
- you prefer Chinese-language ML tutorials with runnable Python examples
- you want a free structured path through classical ML algorithms and intro deep learning
- you want accompanying datasets and video lectures

## When to avoid
- you need production-grade ML libraries rather than learning material
- you only work with modern Python 3, since the core 'Machine Learning in Action' notes target Python 2.7
- you need up-to-date coverage of the latest LLM techniques

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, data-science
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: python
- tags: chinese, tutorials, scikit-learn, pytorch, tensorflow, machine-learning-in-action, study-notes, natural-language-processing

## Member repositories
- apachecn/ailearning (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:08.792656+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 2026-08-29T16:22:42.336320+00:00, confidence not recorded.
  - readme: https://github.com/apachecn/ailearning (fetched 2026-08-28T04:12:08.792656+00:00, sha cfa1447e59d4)
  - homepage: http://ailearning.apachecn.org/ (fetched 2026-08-29T07:46:15.686206+00:00, sha 7fb9d9ecaada)
  - site_page: https://www.apachecn.org/about (fetched 2026-08-29T07:46:15.689513+00:00, sha 211b768594dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.