Guide · Open source
How to use MLAlgorithms: install, run examples and read the code
Updated · written with AI from the project's own documentation and checked against our catalogue data
MLAlgorithms is an open-source Python project with minimal, readable implementations of common machine learning algorithms, built on numpy, scipy and autograd. It is aimed at students and developers who want to see how methods like logistic regression, PCA or k-means work internally. By the end of this guide you will be able to install the project, run its included examples, and know where to look in the code.
MLAlgorithmsrushter/MLAlgorithms · ★ 11.2k · MIT Install options & downloads Quick start
- 1Clone the repository. Run git clone https://github.com/rushter/MLAlgorithms to download the code.
- 2Enter the project folder. Run cd MLAlgorithms so the following commands run in the right place.
- 3Install dependencies. Run pip install scipy numpy to add the required libraries.
- 4Install in development mode. Run python setup.py develop to install the project so you can edit the source.
- 5Run an example. Run python -m examples.linear_models to see a working algorithm in action.
- 6Read and modify an algorithm. Open a file such as mla/kmeans.py, follow the logic, and change it to test your understanding.
What MLAlgorithms is and who it is for
MLAlgorithms is a collection of minimal and clean implementations of machine learning algorithms. The project's stated goal is to help people learn the internals of ML algorithms or implement them from scratch. The code is written to be easier to follow than optimized libraries and easier to modify.
- Linear regression and logistic regression (mla/linear_models.py)
- Random Forests (mla/ensemble/random_forest.py)
- Support vector machines with Linear, Poly and RBF kernels (mla/svm)
- K-Means (mla/kmeans.py) and Gaussian Mixture Model (mla/gaussian_mixture.py)
- K-nearest neighbors (mla/knn.py) and Naive Bayes (mla/naive_bayes.py)
- Principal component analysis (mla/pca.py) and t-SNE (mla/tsne.py)
- Gradient Boosting trees (mla/ensemble/gbm.py)
- Deep learning (MLP, CNN, RNN, LSTM) (mla/neuralnet) and Deep Q learning (mla/rl)
Check the prerequisites before you install
You need Python, pip and git. The README's install steps use pip to add scipy and numpy, and the code assumes familiarity with numpy and the underlying maths. If you have never written Python, learn the basics first.
Install MLAlgorithms by cloning and building from source
The project is installed by cloning the repository and running it in development mode. Run these commands in a terminal, in order.
git clone https://github.com/rushter/MLAlgorithms
cd MLAlgorithms
pip install scipy numpy
python setup.py developRun the included examples without installing
You can try the examples without the installation step. From inside the project folder, run the linear models example as a Python module.
cd MLAlgorithms
python -m examples.linear_modelsRun the examples in Docker
The README also describes a Docker route. Build the image from the project folder, start a shell in the container, and run the same example inside it.
cd MLAlgorithms
docker build -t mlalgorithms .
docker run --rm -it mlalgorithms bash
python -m examples.linear_modelsRead the code and change it
Because each algorithm lives in its own file, you can open one, such as mla/kmeans.py or mla/pca.py, and follow the steps line by line. The project is designed for this kind of experimenting and modifying. If you want to propose a larger change, the README asks you to open an issue first, and it welcomes improvements to code and documentation.
- Open a file under mla/ and compare it with the maths you have learned.
- Change a parameter or a step and re-run an example to see the effect.
- For bigger changes, open an issue before writing code.
What people use it for
- Studying how logistic regression or PCA works step by step for a course.
- Implementing k-means or a Gaussian Mixture Model from scratch to understand clustering.
- Experimenting with a small SVM using the Linear, Poly or RBF kernels.
- Reading the gradient boosting code in mla/ensemble/gbm.py to see how boosted trees are built.
- Modifying a small neural network in mla/neuralnet for quick experiments.
Frequently asked questions
Is MLAlgorithms free to use?
MLAlgorithms is released under the MIT licence, according to the project information. The MIT licence generally allows use, modification and redistribution, but read the licence file in the repository to confirm the terms.
What do I need to install MLAlgorithms?
You need Python, pip and git. The README installs scipy and numpy with pip, then runs python setup.py develop from the project folder.
How do I run the MLAlgorithms examples?
From the MLAlgorithms folder, run python -m examples.linear_models. The README also shows a Docker route using docker build and docker run.
Can I use MLAlgorithms in production?
The project is not intended for production systems that need speed or scale, because the code favours clarity over optimisation. It also does not describe a release process, so it is not a maintained, stable library.
How does MLAlgorithms compare to scikit-learn, PyTorch or TensorFlow?
Those are optimised, widely used libraries for real projects. MLAlgorithms is for reading and learning how algorithms work, so it trades speed and features for clear, short code.
Do I need to know Python well to use it?
Yes, the code assumes familiarity with Python, numpy and the underlying maths. Complete beginners with no Python experience will find it hard to follow.
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Our verdict
Use MLAlgorithms if you want to learn how machine learning algorithms work by reading short, clean Python code, or if you want to implement methods from scratch. Pick scikit-learn, PyTorch or TensorFlow if you need speed, scale, a stable release history or a well-tested library for production work.
Install options & downloads