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Data Science with .NET and Polyglot Notebooks

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

C#

Tools

Codespaces

Concepts

Artificial Intelligence

Author (1):

Matt Eland

View More author details

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

Why subscribe?

21. Other Books You May Enjoy

Technical requirements

This chapter assumes you have installed Polyglot Notebooks and VS Code. See Chapter 2 for additional details on this process.

You can find the notebook for this chapter on GitHub at https://github.com/PacktPublishing/Data-Science-with-.NET-and-Polyglot-Notebooks in the Chapter14 folder.

Because this chapter is based on preview versions of the extensions API, the final shipped version may be slightly different than the code shown here. See the Further reading section for links to up-to-date documentation to help with any differences between the preview and final version.

The rest of the chapter is locked

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Authors (1)

Matt Eland

Matt Eland

Matt Eland is a senior software engineering and data science consultant at Leading EDJE in Columbus, Ohio. He loves sharing his journey by teaching software engineering, AI, and data science concepts in the most engaging ways possible. Matt has used machine learning to settle debates over whether certain movies are Christmas movies, reinforcement learning to create digital attack squirrels, data analytics to suggest improvements to his favorite TV show, and AI agents to play board games and create an AI agent with the personality of a dog. He is the author of multiple books and courses, helps organize a user group and regional conferences, holds a Master's of Science in Data Analytics, and is a 2x Microsoft MVP in AI and .NET.

See other products by Matt Eland

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