MLAlgorithms

Plain Python code implementing classic machine learning algorithms from scratch, for learning how they work.

Stars: 11.2k
Forks: 1.8k
Licence: MIT
Language: Python
Last update: 1 months ago
Type: Learning resource

What it is

This is a collection of machine learning algorithms written in Python with numpy, scipy and autograd, kept short and readable. It covers topics such as linear and logistic regression, random forests, SVMs, k-means, PCA, t-SNE, gradient boosting and simple neural networks. The project aims to help people understand what happens inside these algorithms rather than to serve as a fast production library.

Read the linear model code to see how gradient-based training is written
Run the included examples such as python -m examples.linear_models
Modify an algorithm implementation to test an idea
Compare a from-scratch implementation against a standard library result

Great for

Students who want to see how algorithms work step by step
Developers who want to implement ML methods from scratch
Engineers who want to read code instead of optimized library internals
People who want small code to experiment with and modify

Not ideal for

Production systems that need speed or scale, since the code favors clarity over optimization
People who need a maintained, well-tested library with a stable release history, since the README does not describe a release process
Beginners with no Python experience, since the code assumes familiarity with numpy and the underlying math

Step-by-step guide: how to use MLAlgorithmsInstall, first run, examples, tips and FAQs

How to get it

1
Clone and build from source
You'll need: Python · pip · git
Clone the repository
git clone https://github.com/rushter/MLAlgorithms
Enter the project folder
cd MLAlgorithms
Install dependencies
pip install scipy numpy
Install in development mode
python setup.py develop
2
Run it
- Clone the repository and enter the folder
- Install scipy and numpy with pip
- Run python setup.py develop
- Run python -m examples.linear_models to see an example
3
Get the most out of it
- Run the Docker setup described in the README if you prefer not to install Python packages locally
- Read the implementation files alongside the algorithm list to match code to theory
- Use the examples folder as a starting point for your own experiments
- Check the neuralnet folder for the deep learning implementations, which the README lists as MLP, CNN, RNN and LSTM

Downloads

Source code (.zip)master branch · straight from GitHub

Strengths

Code is designed to be short and easy to follow, according to the README
Covers a broad range of classic algorithms in one place
Usable without installation by running examples from the repository

Trade-offs

Not optimized, so it is slower than mainstream libraries, as the README implies by contrast
Requires comfort with Python and the math behind each algorithm
The README is brief and does not document an API or test coverage

How it compares

Project	Stars	Type	Level	Updated	Best for
MLAlgorithms (this)	11.2k	Learning resource	intermediate	1 months ago	Students who want to see how algorithms work step by step
annotated_deep_learning_paper_implementations	67.6k	Learning resource	intermediate	4 days ago	Students learning how transformers, GANs, or optimizers work internally
mit-deep-learning	10.5k	Learning resource	beginner	2.8 years ago	Students following MIT deep learning courses
onnxruntime	22.2k	Library	intermediate	4 days ago	Developers who want to deploy a trained model inside an application or service
nni	14.4k	Library	intermediate	2.3 years ago	Researchers comparing hyperparameter tuning algorithms such as TPE, Hyperband, or Bayesian methods
petals	10.7k	Library	intermediate	2.2 years ago	Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.