_LearningMachines

Foundational machine learning engineering packages designed for independent consumption and composition.

Overview

_LearningMachines contains reusable ML primitives, experimentation tools, and workflow-oriented modules for downstream systems.

Packages

• crossval-and-tuning
• data-quality-and-profiling
• data2ditribution-explorer
• decision-trees-from-scratch
• e2e-ml-pipeline
• evaluation-metrics-lab
• feature-engineering-toolkit
• gradient-descent-visualizer
• linear-regression-from-scratch
• model-interpretability-lab
• nlp-classic-playground
• overfitting-playground
• timeseries-forecasting-101

Installation

Option A: consume the whole repository

git submodule add https://github.com/navinBRuas/_LearningMachines.git vendor/learning-machines

Option B: install a single package

pip install git+https://github.com/navinBRuas/_LearningMachines.git#subdirectory=gradient-descent-visualizer
pip install git+https://github.com/navinBRuas/_LearningMachines.git#subdirectory=crossval-and-tuning

Usage

1. Select the package that fits your ML use case.
2. Follow the package README.md for CLI/API examples.
3. Compose packages through explicit boundaries.

Development

• Keep package dependencies minimal and localized.
• Run lint/tests in the target package before merging.
• Update package docs and changelog entries with behavior changes.

Governance & docs

• GOVERNANCE.md
• SECURITY.md
• SUPPORT.md
• CODE_OF_CONDUCT.md

Version

0.1.0

License

See LICENSE.