Learning path: ML engineering

# ML engineering roadmap

## Foundations (1 month)
- Probability + statistics
- Linear algebra basics
- Python + numpy + pandas

## Core ML (2 months)
- Linear / logistic regression
- Decision trees + ensembles
- SVMs
- Validation + leakage

## Deep learning (2 months)
- Backprop fundamentals
- CNNs
- RNNs / Transformers
- Training stability

## MLOps (1 month)
- Feature stores
- Model registry
- Monitoring + drift
- A/B and shadow deploys

## Capstone (1 month)
- End-to-end project
- Write-up + retro