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Data Science with .NET and Polyglot Notebooks

Data Science with .NET and Polyglot Notebooks

By : Matt Eland
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Data Science with .NET and Polyglot Notebooks

Data Science with .NET and Polyglot Notebooks

By: Matt Eland

Overview of this book

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.
Table of Contents (22 chapters)
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1
Part 1: Data Analysis in Polyglot Notebooks
8
Part 2: Machine Learning with Polyglot Notebooks and ML.NET
13
Part 3: Exploring Generative AI with Polyglot Notebooks
16
Part 4: Polyglot Notebooks in the Enterprise

Data Science, Notebooks, and Kernels

Data science seems like it is at an all-time high in popularity as advances in computing, storage, and data analysis have made new types of applications not just possible, but accessible to people who traditionally wouldn’t call themselves data scientists.

This book aims to help developers expand their existing knowledge and capabilities into the fields of data science, machine learning, artificial intelligence (AI), and data analysis.

In this opening chapter, we’ll cover these broad topics and explore what the field of data science includes, how the various parts of data science relate to each other, and how data science notebooks enable you to perform new tasks in new ways.

This chapter covers the following topics:

  • Exploring the field of data science
  • Data science notebooks and Project Jupyter
  • Extending notebooks with kernels
  • Polyglot Notebooks and .NET Interactive
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