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Numpy Ml

Numpy Ml is a Python library of machine-learning algorithms implemented with NumPy, intended as a legible starting point for prototyping and experimentation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Numpy Ml — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to study or adapt NumPy implementations while prototyping, or compare approaches across the algorithm families listed in the project documentation.

Who it is for

  • Python developers exploring or prototyping machine-learning algorithms.
  • Practitioners who want to inspect implementations and adapt them as a starting point for custom code.

Documented uses

  • Explore implementations across neural networks, Bayesian methods, Gaussian processes, trees, linear models and reinforcement learning.
  • Start machine-learning experiments from NumPy-based code.
  • Use preprocessing utilities for tasks such as tokenization, feature standardization and one-hot encoding.
Before you choose

Requirements & limits

The project is publicly available on GitHub and identifies its license as GPL-3.0.

  • The project describes its implementations as inefficient but legible, positioning them for experimentation rather than performance claims.
  • The README documents both a source checkout with development requirements and installation as a Python package.
  • Reinforcement-learning agents use environments defined in OpenAI Gym; the README documents an optional rl package extra.
  • The repository's contribution guidance requires Python standard library and NumPy, with SciPy allowed in special circumstances.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a local checkout for experimentation

Clone the repository, create the documented virtual environment and install the development requirements.

Before you start

  • Python with pip3
  • virtualenv

Steps

  1. Clone https://github.com/ddbourgin/numpy-ml.git.

  2. Change into the numpy-ml directory, create a virtual environment named npml, and activate it.

  3. Install the development requirements with pip3 install -r requirements-dev.txt.

Expected outcome

A cloned project with its documented development requirements installed in the activated virtual environment, ready for local experimentation.

Guide limitations

  • These setup instructions do not specify a particular experiment or model run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
ddbourgin/numpy-ml · Official homepage title · retained in its source language
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