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Artículo: AMZ-B0DFLYXX6G

Data Science with .NET and Polyglot Notebooks: Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

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Paperback

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0.76 kg

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Sí

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Amazon

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USA

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Expand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShellKey FeaturesConduct a full range of data science experiments with clear explanations from start to finishLearn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problemsAccess all of the code online as a notebook and interactive GitHub CodespacePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs 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.What you will learnLoad, analyze, and transform data using DataFrames, data visualization, and descriptive statisticsTrain machine learning models with ML.NET for classification and regression tasksCustomize ML.NET model training pipelines with AutoML, transforms, and model trainersApply best practices for deploying models and monitoring their performanceConnect to generative AI models using Polyglot NotebooksChain together complex AI tasks with AI orchestration, RAG, and Semantic KernelCreate interactive online documentation with Mermaid charts and GitHub CodespacesWho this book is forThis book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.Table of ContentsData Science, Notebooks, and KernelsExploring Polyglot NotebooksGetting Data and Code into Your NotebooksWorking with Tabular Data and DataFramesVisualizing DataVariable CorrelationsClassification Experiments with ML.NET AutoMLRegression Experiments with ML.NET AutoMLBeyond AutoML: Pipelines, Trainers, and TransformsDeploying Machine Learning ModelsGenerative AI in Polyglot NotebooksAI Orchestration with Semantic KernelEnriching Documentation with Mermaid DiagramsExtending Polyglot NotebooksAdopting and Deploying Polyglot Notebooks

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$96,72

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