rushter/mlalgorithms

★ 10,979PythonAudience · developerComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((mlalgorithms))
    What it does
      Readable ML code
      Learning focused
      Not production
    Algorithms
      Neural networks
      Random forests
      Clustering
      Reinforcement learning
    Tech stack
      Python
      NumPy
      Docker
    Audience
      Students
      Developers

mindmap root((mlalgorithms)) What it does Readable ML code Learning focused Not production Algorithms Neural networks Random forests Clustering Reinforcement learning Tech stack Python NumPy Docker Audience Students Developers

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Things people build with this

USE CASE 1

Study how a random forest or neural network works by reading simple, uncommented Python code before using a library like scikit-learn.

USE CASE 2

Run and modify a k-means or SVM implementation to see exactly what happens at each step.

USE CASE 3

Use the Docker setup to experiment with deep Q-learning or t-SNE without touching your local Python environment.

Tech stack

PythonNumPySciPyDocker

Getting it running

Difficulty · easy Time to first run · 5min

Clone the repo and install two Python dependencies, a Docker option is also available.

License not specified in the explanation.

In plain English

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.

Copy-paste prompts

Prompt 1

Show me the rushter/mlalgorithms implementation of k-means clustering and walk me through each line in plain English.

Prompt 2

Using the mlalgorithms repo as a reference, help me build a logistic regression from scratch in Python without any ML libraries.

Prompt 3

Compare the mlalgorithms random forest implementation to scikit-learn's, what shortcuts does scikit-learn take that the educational version skips?

Prompt 4

Help me add a simple gradient descent visualizer to the mlalgorithms linear regression code.

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