Lesson 31 of 40 AI / ML Expert ⏱ 35 min

Machine Learning with scikit-learn

Build end-to-end ML pipelines — preprocessing, feature engineering, classification, regression, model evaluation, and joblib serialization.

Part 1: Introduction to Machine Learning with scikit-learn

Build end-to-end ML pipelines — preprocessing, feature engineering, classification, regression, model evaluation, and joblib serialization.

This lesson uses Python 3.13 features and follows best practices for development in Visual Studio 2026 with Copilot assistance.

Part 2: Core Concepts & Code Examples

# Machine Learning with scikit-learn — Python 3.13 Example
from typing import Any

def main() -> None:
    """Entry point demonstrating lesson 31 concepts."""
    print(f"Lesson 31: Machine Learning with scikit-learn")

if __name__ == "__main__":
    main()

Part 3: Best Practices & Patterns

Apply the patterns from this lesson consistently across your projects. Visual Studio 2026's Python IntelliSense, type checking integration, and GitHub Copilot will guide you toward idiomatic, production-ready Python 3.13 code.

Use type hints for all function signatures
Write docstrings with Args/Returns sections
Run ruff for linting, mypy for type checking
Test every function with at least one pytest test

Part 4: Next Steps

Practice these concepts hands-on, then continue to Lesson 32. Return to Python Tutorial Home to see the full curriculum, or visit VisualStudioTutor.com for Visual Studio 2026 guides.