Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
Arrow right icon
View More author details
Toc

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

To get the most out of this book

This book is intended for software developers familiar with the basics of .NET development in C# trying to learn more about data science in the .NET ecosystem. The ideal reader will be familiar with basic programming concepts and syntax related to C# and curious about learning more about data science.

Although the book covers some F#, I do not expect most readers to be familiar with the language and its syntax, so all F# syntax will be explained.

The following software has been covered in the book:

  • VS Code
  • .NET (C# and some F#)
  • ML.NET
  • Other languages covered in brief: PowerShell, SQL, and KQL

And, you can use any of the following operating systems for this book:

  • Windows
  • MacOS
  • Linux

In order to run code online in GitHub Codespaces, you will need to create a free GitHub account.

Chapters 11 and 12 refer to resources available in Azure and OpenAI. These resources may involve minimal costs and require creating an account. However, these chapters can still be understood and experienced without running the code.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime