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
    • PCA
  • 📊 Evaluation Metrics included (Accuracy, Precision, Recall, etc.)
  • 🧹 Preprocessing utilities
  • 🪶 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-0.1.7.tar.gz (10.4 kB view details)

Uploaded Source

Built Distribution

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

embeddedml-0.1.7-py3-none-any.whl (14.2 kB view details)

Uploaded Python 3

File details

Details for the file embeddedml-0.1.7.tar.gz.

File metadata

  • Download URL: embeddedml-0.1.7.tar.gz
  • Upload date:
  • Size: 10.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for embeddedml-0.1.7.tar.gz
Algorithm Hash digest
SHA256 b06093fecd84f474c7a223989684919227d74efa96ab501d52d36f6dcc6d6896
MD5 0db9cc2a7f12fa7d32231580449a80e7
BLAKE2b-256 6d86cb5a6618d320a90661a6bfe77393405a73b2497320e087f875255fbc2089

See more details on using hashes here.

File details

Details for the file embeddedml-0.1.7-py3-none-any.whl.

File metadata

  • Download URL: embeddedml-0.1.7-py3-none-any.whl
  • Upload date:
  • Size: 14.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for embeddedml-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 1b7202f720c050c0e7ac69286936488ede726b1753aba285fde38f7e9d7156ec
MD5 df8674c12e051007ddb19631c11ac340
BLAKE2b-256 c69788c0c54441c9f410336cd413e784729295750c463a9b5506dcf82aed0b47

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