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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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
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Table of Contents (22) Chapters Close

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 21. Other Books You May Enjoy

Preface

Polyglot Notebooks and its .NET Interactive kernels let you mix together code and markdown cells in an interactive and highly visual notebook experience. By supporting rich documentation and putting the results of each code cell below the cell, Polyglot Notebooks makes it easy for developers to quickly execute code and see results, then make adjustments and try again.

This makes Polyglot Notebooks ideal for experimentation, documentation, and iterative and analytical tasks such as data analysis, machine learning, and artificial intelligence applications.

This book will teach you the Polyglot Notebooks environment and then use that as a basis to explore and learn data analysis, machine learning, and generative AI.

By the time you complete this book, you’ll have learned the basics of machine learning in .NET with ML.NET, data analysis and visualization with DataFrames and Plotly.NET, generative AI with Azure OpenAI and Semantic Kernel, and how these things can tie together to improve and expand your capabilities as a growing developer or data scientist.

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