Machine learning workshops. From fundamentals to production systems.
Where machine learning began. Understand how machines learn from examples by implementing the 1957 algorithm that started it all.
| Notebook | Description | Colab |
|---|---|---|
04_perceptron.ipynb |
Perceptron from scratch, Iris dataset, XOR problem |  |
Resources:
Build a production-ready RAG (Retrieval Augmented Generation) system. Why simple RAG fails and how to fix it.
| Notebook | Description | Colab |
|---|---|---|
01_simple_rag.ipynb |
Basic RAG implementation | |
02_production_rag.ipynb |
Full pipeline with all stages | |
03_evaluation.ipynb |
Testing and debugging | |
Resources:
# In Google Colab, run:
!git clone https://github.com/i33ym/workshop.git
%cd workshop- Python 3.8+
- NumPy, pandas, matplotlib (for perceptron)
- OpenAI API key (for RAG notebooks)
- Google account (for Colab)