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
1 maintainer
Metadata
1 maintainer
Release files for embeddedML 0.1.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| embeddedml-0.1.7.tar.gz | 10.4 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| embeddedml-0.1.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.6 kB
Release files / embeddedml-0.1.7.tar.gz
| Download URL | embeddedml-0.1.7.tar.gz |
|---|---|
| Size | 10.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/6.1.0 CPython/3.12.2
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Release files / embeddedml-0.1.7-py3-none-any.whl
| Download URL | embeddedml-0.1.7-py3-none-any.whl |
|---|---|
| Size | 14.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.12.2
|
Security for embeddedML 0.1.7
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No provenance found
None of the files in this release were uploaded with provenance metadata.
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