Skip to main content

Optimized Machine Learning Library for Embedded Systems

Project description

EmbeddedML

EmbeddedML is an optimized and lightweight machine learning library designed specifically for embedded systems (such as Raspberry Pi, Jetson Nano, Orange Pi, etc.).
This library implements core ML algorithms with minimal dependencies, making it suitable for resource-constrained environments.


🚀 Features

  • ✔️ Support for classic machine learning algorithms:
    • SVM (Support Vector Machines)
    • KNN (K-Nearest Neighbors)
    • Naive Bayes
    • Logistic Regression
    • Simple & Multiple Linear Regression
    • Polynomial Regression
    • K-Means Clustering
  • 📊 Evaluation Metrics included (Accuracy, Precision, Recall, etc.)
  • 🧹 Preprocessing utilities
  • 🐍 Pure Python implementation – no external dependencies
  • 🪶 Lightweight and fast

📦 Installation

Install the package via pip:

pip install EmbeddedML

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

embeddedml-0.1.0.tar.gz (6.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

embeddedml-0.1.0-py3-none-any.whl (9.1 kB view details)

Uploaded Python 3

File details

Details for the file embeddedml-0.1.0.tar.gz.

File metadata

  • Download URL: embeddedml-0.1.0.tar.gz
  • Upload date:
  • Size: 6.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for embeddedml-0.1.0.tar.gz
Algorithm Hash digest
SHA256 1165c1ffbe4b0cb3f30bb27dd542f6fb5a2267defd040331a048132d38928965
MD5 a25880b44c1697f36cf144c66de10f2e
BLAKE2b-256 e8ba753640fa5245489693573f64bbec7b77de5516250acbf4b18786b4fe9f72

See more details on using hashes here.

File details

Details for the file embeddedml-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: embeddedml-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 9.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for embeddedml-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d48154fa4c508798399de81ab2f293774e80efff38f5e621e444b86d80d6dc56
MD5 6fbfdf50078fe70d40d2557f67092174
BLAKE2b-256 68ad971aa8ed05a26a88e7a3ef5014d88630669a34796fdbed514f714876af9e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page