Homemade Machine Learning – Python Implementations of Popular Algorithms with Jupyter Notebooks

The article promotes the GitHub project Homemade Machine Learning , which offers pure‑Python implementations of popular machine‑learning algorithms together with interactive Jupyter Notebook demonstrations that let users train models, adjust configurations, and view results, charts, and predictions directly in the browser.

The project's goal is to build each algorithm from scratch to deepen understanding of the underlying mathematics, rather than relying on third‑party libraries; therefore the implementations are labeled “homemade” and are not intended for production use.

In supervised learning, the article explains the input‑to‑output mapping concept and covers regression (linear, multivariate, and nonlinear) with examples such as stock‑price forecasting, as well as classification, focusing on logistic regression with demos ranging from iris‑flower classification to MNIST digit and clothing recognition.

For unsupervised learning, it introduces clustering (K‑means) and anomaly detection using Gaussian distributions, providing use‑case examples like market segmentation, network analysis, intrusion detection, and fraud detection, each accompanied by mathematical references, code links, and live demos.

The neural‑network section describes neural networks as a framework for handling complex data, highlighting the multilayer perceptron (MLP) with Jupyter demos for handwritten digit and clothing classification.

Learning prerequisites are listed: install Python, then install all required packages with pip install -r requirements.txt , and start a local or remote Jupyter server to run the notebooks.

Data sets used by the project can be found at https://github.com/trekhleb/homemade-machine-learning/tree/master/data . The article also includes promotional QR codes for a free Python course and additional learning resources.