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pdf | 34.05 MB | English| Isbn: 9781835882979 | Author: Matt Eland | Year: 2024
Description :
Expand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShell
Key Features
[*]Conduct a full range of data science experiments with clear explanations from start to finish
[*]Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems
[*]Access all of the code online as a notebook and interactive GitHub Codespace
[*]Purchase of the print or Kindle book includes a free PDF eBook
Book Description
As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft's .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you'll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you'll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.
What you will learn
[*]Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics
[*]Train machine learning models with ML.NET for classification and regression tasks
[*]Customize ML.NET model training pipelines with AutoML, transforms, and model trainers
[*]Apply best practices for deploying models and monitoring their performance
[*]Connect to generative AI models using Polyglot Notebooks
[*]Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel
[*]Create interactive online documentation with Mermaid charts and GitHub Codespaces
Who this book is for
This book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It's ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.
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