MLE — Machine Learning Engineering from First Principles

Structure:

MLE/
├── README.md                # Overview and setup
├── requirements.txt         # Dependencies
│
├── advanced_ml/             # Experimental work (in-progress)
│   ├── 01-neural-nets/      # Neural networks from scratch
│   └── 02-pytorch-opencv/   # PyTorch and OpenCV deep dives
│
├── ml/                      # Stable, polished content
│   ├── 01-docs/             # Tutorials and conceptual notes
│   ├── 02-implementations/  # Algorithm implementations (e.g., regression, PCA)
│   ├── 04-kaggle/           # Kaggle exercises (e.g., Intermediate ML)
│   └── 05-projects/         # End-to-end projects (e.g., time series, NLP)
│
├── datasets/                # Datasets (small files + external links)
│   └── archive/             # Example: heart_failure_clinical_records_dataset.csv
│
├── programming/             # Language-specific resources
│   ├── python/              # Python for ML (OOP, modularization)
│   └── sql/                 # SQL scripts for data science
│
└── tools/                   # Utility scripts
    ├── scaffold_topics.py   # Create numbered topic folders
    └── fix_notebook_links.py # Update references in `.md`/`.ipynb` files

---

Key Areas

1. Stable Content (ml/)

   • Tutorials: ml/01-docs/
   • Implementations: ml/02-implementations/
   • Projects: ml/05-projects/

2. Experimental Work (advanced_ml/)

   • Neural networks: advanced_ml/01-neural-nets/
   • PyTorch/OpenCV: advanced_ml/02-pytorch-opencv/

3. Utilities (tools/)

   • Scaffolding: scaffold_topics.py
   • Link fixer: fix_notebook_links.py

---

Quick Start

git clone https://github.com/Savvythelegend/MLE.git
cd MLE
jupyter notebook ml/02-implementations/implementations/regression/logistic_regression.ipynb

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