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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

Evaluating binary classification models

Model metrics tell you how accurate your models are and some of the areas they struggle in. In classification problems, many of these metrics can be embodied into a single graphic – the confusion matrix.

Confusion matrixes take the form of a table that tells you how your model classifies test data and what data points its getting confused by and incorrectly classifying.

In machine learning, we refer to our predictions as either true or false, based on their correctness, and positive or negative, based on our prediction. This is shown in the following table:

Predicted true

Predicted false

Actually true

True positive

False negative

Actually false

False positive

True negative

Table...

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83
Tech Concepts
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Programming languages
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