# Machine Learning

Scikit-learn pipelines, model selection, ensembles, imbalanced datasets, and interpretability.

Use this subtrack for applied classical ML portfolio work. It is the fastest path in this phase if you want interview-ready projects and strong end-to-end workflow practice without starting from deep learning.

## How To Use This Subtrack Well

- Build one full supervised learning project from raw data to stakeholder-facing results.
- Focus on validation, leakage prevention, and interpretability rather than model-count collection.
- Pair this subtrack with [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) and [../../09-mlops/README.md](../../09-mlops/README.md) for stronger production habits.

## What Comes Next

- Continue to [../README.md](../README.md) for the broader practical data science roadmap.
- Continue to [../../09-mlops/README.md](../../09-mlops/README.md) if you want to productionize one of your projects.
- Continue to [../../29-ai-hardware-llm-validation/README.md](../../29-ai-hardware-llm-validation/README.md) only if your interest shifts toward systems rather than modeling.
