zotroneneis/machine_learning_basics resource

Health v2 · maintenance only

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4416 stars · 833 forks observed · 2026-08-28

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

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

learning-resource · maturity stable

machine-learning data-science machine-learning education tutorials python jupyter-notebooks from-scratch-implementations algorithms educational

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