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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

Understanding machine learning

Machine learning uses mathematics to help understand patterns in data and make decisions based on those patterns.

There are several different branches of machine learning, including the following:

  • Supervised learning, which involves using historical data to predict new values
  • Unsupervised learning, which is typically used to cluster data points together based on similarity or to spot anomalies
  • Reinforcement learning and semi-supervised learning, which involve systems that iteratively experiment and adapt based on the results of their experiments.

Typically, when people mention machine learning, they are referring to supervised learning, as this is the most common application of machine learning and more specific terms are usually used for the other forms of machine learning.

We'll focus on supervised learning in this book and its two most common tasks.

Supervised learning

Supervised learning is the art of training...

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