# zotroneneis/machine_learning_basics

Plain python implementations of basic machine learning algorithms

Repository: https://github.com/zotroneneis/machine_learning_basics
Canonical: https://ross.abutalabs.com/products/machine_learning_basics
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, logistic-regression, ipynb, machine-learning-algorithms, linear-regression, perceptron, python-implementations, kmeans, algorithm, python3, python, neural-network, k-nearest-neighbours, k-nearest-neighbor, k-nn, neural-networks
Last push: 2024-06-27T11:21:33+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3117, "days_push": 797, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4416, forks 833 (observed 2026-08-28T04:08:48.684689+00:00)

## What it is
A collection of Jupyter notebooks implementing basic machine learning algorithms from scratch in plain Python, without ML libraries. It covers algorithms like linear/logistic regression, decision trees, k-means, SVMs, and neural networks for educational understanding.

## Use cases
- learn how machine learning algorithms work internally
- see plain python implementations of linear regression
- understand neural networks from scratch
- study k-means clustering implementation
- learn decision trees for classification and regression
- understand support vector machines basics
- learn data preprocessing for machine learning

## When to choose
- you want to understand the underlying math and structure of ML algorithms
- you prefer educational, readable code over efficient implementations
- you want to run notebooks online via Binder without setup

## When to avoid
- you need production-ready or performant ML implementations
- you want a library to use in real applications
- you need GPU-accelerated or scalable training

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python
- tags: jupyter-notebooks, from-scratch-implementations, algorithms, educational

## Member repositories
- zotroneneis/machine_learning_basics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.684689+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 2026-08-29T18:21:01.021887+00:00, confidence not recorded.
  - readme: https://github.com/zotroneneis/machine_learning_basics (fetched 2026-08-28T04:08:48.684689+00:00, sha cda392ab76d8)
- Data as of 2026-08-30T08:39:29.467469+00:00.