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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

Selecting regression algorithms

By default, ML.NET supports the following regression model trainers:

  • FastTree: A decision tree algorithm, which we discussed earlier in this chapter. Decision trees are fast, simple, and easy to understand, and they are not sensitive to scaling. However, if you allow them a high number of leaves, they may overfit the training data at the expense of accuracy on data they’ve not seen.
  • FastTreeTweedie: Almost identical to FastTree but built for a specific distribution of data, referred to as a Tweedie distribution. See the Further reading section for more details.
  • LightGbm: Uses gradient boosting and Microsoft’s LightGbm framework for high-performance decision trees.
  • FastForest: A random forest algorithm. This approach uses a voting ensemble of decision trees, with each tree’s prediction constituting part of the overall “vote” for the final prediction.
  • LbfgsPoissonRegression: A linear algebra approach...
CONTINUE READING
83
Tech Concepts
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Programming languages
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Data Science with .NET and Polyglot Notebooks
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