# luwill/Machine_Learning_Code_Implementation

Mathematical derivation and pure Python code implementation of machine learning algorithms.

Repository: https://github.com/luwill/Machine_Learning_Code_Implementation
Canonical: https://ross.abutalabs.com/products/machine_learning_code_implementation
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, python, jupyter-notebook
Last push: 2026-04-29T07:10:57+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 79, release rhythm 35, longevity 100
- inputs: {"age_days": 2758, "days_push": 126, "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 1551, forks 586 (observed 2026-08-28T04:05:02.316135+00:00)

## What it is
A companion code repository for a Chinese machine learning book that provides mathematical derivations and pure Python implementations of 26 classic algorithms across supervised, ensemble, unsupervised, and probabilistic models. It supplements textbooks like 'Statistical Learning Methods' and Zhou Zhihua's 'Machine Learning' (watermelon book) with runnable Jupyter Notebook code organized by chapter.

## Use cases
- learn machine learning algorithms from scratch with math derivations
- find pure Python implementations of classic ML algorithms
- supplement study of the watermelon book or Statistical Learning Methods
- study perceptron, logistic regression, LDA, HMM, MCMC implementations
- get example code for supervised and unsupervised learning models
- prepare for ML interviews by understanding algorithm internals

## When to choose
- you want to understand the math behind ML algorithms alongside working code
- you are studying Chinese ML textbooks and need a code companion
- you prefer minimal pure Python implementations over heavy framework code

## When to avoid
- you need production-ready, well-tested ML libraries
- you want GPU-accelerated or deep learning frameworks
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, education, tutorials
- platform: python
- tags: jupyter-notebook, algorithm-derivations, companion-code, textbook, educational

## Member repositories
- luwill/Machine_Learning_Code_Implementation (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.316135+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 2026-08-30T04:30:16.393809+00:00, confidence not recorded.
  - readme: https://github.com/luwill/Machine_Learning_Code_Implementation (fetched 2026-08-28T04:05:02.316135+00:00, sha 28838147e88a)
- Data as of 2026-08-30T08:39:29.467469+00:00.