---
name: Machine Learning
description: Python machine learning with scikit-learn, PyTorch, and TensorFlow
version: "2.1.0"
sasmp_version: "1.3.0"
bonded_agent: 03-data-science
bond_type: PRIMARY_BOND

# Skill Configuration
retry_strategy: exponential_backoff
observability:
  logging: true
  metrics: model_accuracy
---

# Python Machine Learning Skill

## Overview
Build machine learning models using Python libraries including scikit-learn, PyTorch, and supporting tools.

## Topics Covered

### Scikit-learn
- Data preprocessing
- Model selection
- Training pipelines
- Cross-validation
- Hyperparameter tuning

### PyTorch Basics
- Tensor operations
- Neural network modules
- Training loops
- DataLoader usage
- GPU acceleration

### Feature Engineering
- Feature selection
- Dimensionality reduction
- Feature scaling
- Encoding techniques
- Missing data handling

### Model Evaluation
- Metrics selection
- Confusion matrix
- ROC curves
- Learning curves
- Model comparison

### MLOps Basics
- Model serialization
- Experiment tracking (MLflow)
- Model versioning
- Serving models
- Reproducibility

## Prerequisites
- Python fundamentals
- NumPy and Pandas
- Statistics basics

## Learning Outcomes
- Train ML models
- Evaluate model performance
- Build ML pipelines
- Deploy models to production