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Mlalgorithms

Mlalgorithms is a public, MIT-licensed Python repository of compact from-scratch implementations of machine-learning algorithms, including neural networks, regression, ensembles, SVMs, clustering, Bayesian methods, dimensionality reduction, gradient boosting and deep Q-learning.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mlalgorithms — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it when the goal is to inspect, teach or experiment with algorithm implementations from source, rather than rely only on optimized library abstractions.

Who it is for

  • People learning machine-learning internals
  • Instructors creating algorithm explanations
  • Python developers conducting small educational experiments
  • Contributors improving implementations or documentation

Documented uses

  • Read understandable implementations of common machine-learning methods.
  • Run included examples from a cloned repository.
  • Compare algorithm behavior in small learning or benchmarking exercises.
  • Adapt selected implementations into focused educational Python material.
Before you choose

Requirements & limits

The repository is publicly visible on GitHub, and the README documents Git-based setup, direct example execution and Docker execution. It is released under the MIT license.

  • Python is the implementation language.
  • NumPy, SciPy and Autograd are the documented dependencies.
  • The collection covers supervised learning, unsupervised learning, deep learning and reinforcement learning examples.
  • The README documents both development installation and execution without installation.
  • Docker commands are provided for running the examples in a container.

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

Set up Mlalgorithms and run a documented example

Clone the repository, install its documented dependencies and execute the linear-model example.

Before you start

  • Git
  • Python
  • NumPy
  • SciPy
  • A cloned copy of the Mlalgorithms repository

Steps

  1. Clone the repository and change into its directory.

  2. Install SciPy and NumPy.

  3. Run the development installation with python setup.py develop.

  4. Run the linear-model example with python -m examples.linear_models.

Expected outcome

The documented linear-model example is started from the repository after development installation.

Guide limitations

  • The evidence documents this example workflow but does not establish results, performance, compatibility guarantees or production suitability.
Documentation 1 ↗
Source-based practical guide

Run the examples in Docker

Build the documented Docker image and launch the repository example environment.

Before you start

  • Docker
  • A cloned copy of the Mlalgorithms repository

Steps

  1. Change into the repository directory.

  2. Build an image tagged mlalgorithms with docker build -t mlalgorithms ..

  3. Start the container with docker run --rm -it mlalgorithms bash.

  4. Run python -m examples.linear_models inside the container.

Expected outcome

A containerized environment is provided for executing the documented linear-model example.

Guide limitations

  • The source documents the Docker commands but does not provide image details, execution results or compatibility guarantees.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
rushter/MLAlgorithms · Official homepage title · retained in its source language
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