#2323rd of 23 in Learning and cookbooks
homemade-machine-learning
From-scratch Python implementations of classic ML algorithms with Jupyter demos
- Stars
- 24.8k
- License
- MIT
- Last commit
- Nov 2025
- Language
- Jupyter Notebook
Missed the latest GitHub check; these facts may be older.
Overview
Educational repository implementing linear regression, logistic regression, k-means, Gaussian anomaly detection and a multilayer perceptron in Python, each with a math write-up. Every algorithm has a Jupyter notebook demo on datasets such as MNIST, Fashion MNIST and Iris, viewable in NBViewer or runnable in Binder. The author states the code is for learning, not production.
Who it is for: Students learning the math behind classic machine learning algorithms
Strengths
- Each algorithm has a math explanation, a Python implementation and a notebook demo
- Notebooks open in NBViewer or Binder without a local install
- Demos use concrete datasets: MNIST, Fashion MNIST, Iris, country happiness
Weaknesses
- Author states the implementations are not intended for production use
- Covers only a handful of classic algorithms; no CNNs, transformers or LLMs
- Explanations largely follow Andrew Ng's Coursera course
- No releases; last commit 2025-11-23
What it needs
- no GPU
- Needs Python, Jupyter Notebook
- port 8888
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