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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

Beyond AutoML: Pipelines, Trainers, and Transforms

We’ve now covered regression and classification using ML.NET’s AutoML experiments API. In the previous two chapters, we focused more on the task that we wanted to accomplish than the actual process of the machine learning logic, and with good reason – there’s a lot to learn when learning about machine learning.

In this chapter, we’ll lift the veil on what AutoML does by recreating our regression experiment from Chapter 8 without using AutoML at all. This will show us the important role of pipelines, trainers, and transforms in the model training process.

Once we have a firm understanding of ML.NET without AutoML, we’ll see how advanced AutoML experiments can be configured by using AutoML pipelines. We’ll see how this approach gives you more fine-grained control of the training process while still taking advantage of AutoML’s hyperparameter tuning abilities.

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