MLAlgorithms
Minimal and clean examples of machine learning algorithms implementations
Clean, readable Python implementations of common machine learning algorithms designed for learning how they work, not for production use.
MLAlgorithms is a Python repository containing clean, readable implementations of common machine learning algorithms. The goal is to help people understand how these algorithms work under the hood, rather than providing the most optimized production-ready versions. The code is intentionally kept simple so it is easier to read and experiment with than what you find in popular libraries.
The collection covers a wide range of techniques. On the deep learning side there are multi-layer neural networks, convolutional networks (used in image recognition), recurrent networks (used for sequential data), and long short-term memory networks. On the classical machine learning side there are linear and logistic regression, random forests, support vector machines, k-means clustering, k-nearest neighbors, naive Bayes classification, principal component analysis, gradient boosting, and reinforcement learning via deep Q-learning. There is also a Gaussian mixture model, restricted Boltzmann machine, t-SNE (a technique for visualizing high-dimensional data), and factorization machines.
All implementations use Python with a small set of standard numerical computing libraries. Installation is straightforward: clone the repository, install two dependencies, and run any of the provided examples. A Docker-based option is also available if you prefer not to install anything directly on your machine.
The project is aimed at students and developers learning machine learning who want to see how these methods work at a conceptual level before relying on higher-level libraries.
Where it fits
- Study how a random forest or neural network works by reading simple, uncommented Python code before using a library like scikit-learn.
- Run and modify a k-means or SVM implementation to see exactly what happens at each step.
- Use the Docker setup to experiment with deep Q-learning or t-SNE without touching your local Python environment.