You're reading from Data Science with .NET and Polyglot Notebooks Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835882962

Length 404 pages

Edition 1st Edition

Languages

Tools

Codespaces

Concepts

Artificial Intelligence

Author (1):

Matt Eland

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Table of Contents (22) Chapters

Preface

1. Part 1: Data Analysis in Polyglot Notebooks

2. Chapter 1: Data Science, Notebooks, and Kernels FREE CHAPTER

3. Chapter 2: Exploring Polyglot Notebooks

4. Chapter 3: Getting Data and Code into Your Notebooks

5. Chapter 4: Working with Tabular Data and DataFrames

6. Chapter 5: Visualizing Data

7. Chapter 6: Variable Correlations

8. Part 2: Machine Learning with Polyglot Notebooks and ML.NET

9. Chapter 7: Classification Experiments with ML.NET AutoML

10. Chapter 8: Regression Experiments with ML.NET AutoML

11. Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms

12. Chapter 10: Deploying Machine Learning Models

13. Part 3: Exploring Generative AI with Polyglot Notebooks

14. Chapter 11: Generative AI in Polyglot Notebooks

15. Chapter 12: AI Orchestration with Semantic Kernel

16. Part 4: Polyglot Notebooks in the Enterprise

17. Chapter 13: Enriching Documentation with Mermaid Diagrams

18. Chapter 14: Extending Polyglot Notebooks

19. Chapter 15: Adopting and Deploying Polyglot Notebooks

20. Index

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21. Other Books You May Enjoy

Summary

In this chapter, we saw how your data science notebooks can move beyond VS Code and be integrated into Jupyter Notebooks or JupyterLab, or deployed to GitHub Codespaces.

We also discussed ways that notebooks can help various people inside and outside your organization with data analysis, data science, and software engineering tasks.

We closed the chapter – and the book – with discussions on where AI, machine learning, and data analytics can fit into existing tech careers – and some directions you can take as you explore how these concepts fit into your own interests and career direction.

As we now close this book, I’d like to leave you with some parting words.

I sincerely hope that you find data analysis, machine learning, and AI to be as interesting and fascinating as I do. These fields offer a multitude of interesting tools and approaches for solving unusual problems – and problems that are traditionally difficult for software...