Homemade Machine Learning: Python Implementations of Popular Algorithms with Jupyter Demos

This article introduces the GitHub repository “Homemade Machine Learning,” which offers pure‑Python implementations of popular machine learning algorithms, aiming to teach the underlying mathematics rather than provide production‑ready code.

Supervised learning covers regression (linear regression with examples such as GDP‑based happiness index prediction) and classification (logistic regression with demos on Iris flower classification and MNIST digit recognition). Each algorithm is linked to mathematical references, source code, and interactive Jupyter Notebook demos.

Unsupervised learning includes clustering (K‑means with examples in market segmentation) and anomaly detection (Gaussian‑based detection for intrusion or fraud). Again, math, code, and demo links are provided.

Neural networks are presented as the multilayer perceptron (MLP) framework, with notebooks for MNIST digit and clothing classification.

The article also provides practical setup instructions: install Python, then run pip install -r requirements.txt to obtain all dependencies, and launch Jupyter locally or remotely. Dataset links and additional resources are listed.

Images and QR‑code sections promote a free Python course and additional learning materials, but the core content remains an educational guide to implementing and experimenting with machine‑learning algorithms from scratch.