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Data Science with .NET and Polyglot Notebooks
Data Science with .NET and Polyglot Notebooks

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

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Data Science with .NET and Polyglot Notebooks

Part 1: Data Analysis in Polyglot Notebooks

We’ll start our journey by introducing Polyglot Notebooks and their role in software engineering, data analysis, and machine learning workflows. We’ll cover the Polyglot Notebooks technology and user interface as well as the basic decisions and actions you’ll make working with notebooks.

The remainder of this part focuses on loading data into a notebook and then performing data analysis in Polyglot Notebooks using .NET tooling using C# and F#.

Whether you’re an experienced data analyst or have no prior knowledge, you’ll learn how to load up, analyze, clean, and manipulate data using .NET technologies like the DataFrame.

You’ll also see how you can effectively understand data distributions and create helpful visuals using libraries like Plotly.NET, Microsoft.Data.Analysis, and MathNet.Numerics.

This part has the following chapters:

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 30, 2024
Length: 404 pages
Edition : 1st
Language : English
ISBN-13 : 9781835882979
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Product Details

Publication date : Aug 30, 2024
Length: 404 pages
Edition : 1st
Language : English
ISBN-13 : 9781835882979
Category :
Languages :
Tools :

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Table of Contents

21 Chapters
Part 1: Data Analysis in Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 1: Data Science, Notebooks, and Kernels Chevron down icon Chevron up icon
Chapter 2: Exploring Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 3: Getting Data and Code into Your Notebooks Chevron down icon Chevron up icon
Chapter 4: Working with Tabular Data and DataFrames Chevron down icon Chevron up icon
Chapter 5: Visualizing Data Chevron down icon Chevron up icon
Chapter 6: Variable Correlations Chevron down icon Chevron up icon
Part 2: Machine Learning with Polyglot Notebooks and ML.NET Chevron down icon Chevron up icon
Chapter 7: Classification Experiments with ML.NET AutoML Chevron down icon Chevron up icon
Chapter 8: Regression Experiments with ML.NET AutoML Chevron down icon Chevron up icon
Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms Chevron down icon Chevron up icon
Chapter 10: Deploying Machine Learning Models Chevron down icon Chevron up icon
Part 3: Exploring Generative AI with Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 11: Generative AI in Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 12: AI Orchestration with Semantic Kernel Chevron down icon Chevron up icon
Part 4: Polyglot Notebooks in the Enterprise Chevron down icon Chevron up icon
Chapter 13: Enriching Documentation with Mermaid Diagrams Chevron down icon Chevron up icon
Chapter 14: Extending Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 15: Adopting and Deploying Polyglot Notebooks Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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