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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

Summary

In this chapter, we discussed the high-level fields of data science, machine learning, and AI.

While we covered these earlier in more depth, here is a consolidated set of definitions:

  • Artificial intelligence is the broadest field and revolves around emulating aspects of behaviors found in humans and animals.
  • Data science is the discipline of preparing and analyzing large amounts of data to extract insights and determine future behavior through machine learning and predictive modeling.
  • Machine learning is a broad field involving applying mathematics and statistics to solve data problems. Machine learning includes supervised learning, unsupervised learning, and semi-supervised learning including reinforcement learning.
  • Supervised learning involves applying statistical and mathematical techniques to model trends and relationships found in datasets.

In this chapter, we also discussed the role of notebooks in data science for conducting iterative experiments...

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