Data Science with .NET and Polyglot Notebooks (notice n° 79838)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03513cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88958288 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108004803.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2024 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781835882962 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88958288 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Eland, Matt |
| 245 01 - TITLE STATEMENT | |
| Title | Data Science with .NET and Polyglot Notebooks |
| Remainder of title | Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel |
| Statement of responsibility, etc. | ['Eland, Matt'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Eland, Matt |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88958288">https://international.scholarvox.com/netsen/book/88958288</a> |
| Electronic format type | text/html |
| Host name | |
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