Data Science with .NET and Polyglot Notebooks: Programmer's guide to data science using ML.NET

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.
Table of Contents

Data Science, Notebooks, and Kernels
Exploring Polyglot Notebooks
Getting Data and Code into Your Notebooks
Working with Tabular Data and DataFrames
Visualizing Data
Variable Correlations
Classification Experiments with ML.NET AutoML
Regression Experiments with ML.NET AutoML
Beyond AutoML: Pipelines, Trainers, and Transforms
Deploying Machine Learning Models
Generative AI in Polyglot Notebooks
AI Orchestration with Semantic Kernel
Enriching Documentation with Mermaid Diagrams
Extending Polyglot Notebooks
Adopting and Deploying Polyglot Notebooks