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Author: Matt Eland
Publisher: Packt Publishing Ltd
ISBN: 1835882978
Category : Computers
Languages : en
Pages : 404
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Book Description
ProgExpand 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 PowerShell Key Features Learn Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is for This 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.rammer’s guide to data science using ML.NET, OpenAI, and Semantic Kernel
Author: Matt Eland
Publisher: Packt Publishing Ltd
ISBN: 1835882978
Category : Computers
Languages : en
Pages : 404
Get Book Here
Book Description
ProgExpand 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 PowerShell Key Features Learn Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is for This 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.rammer’s guide to data science using ML.NET, OpenAI, and Semantic Kernel
Author: Julian Soh
Publisher: Springer Nature
ISBN:
Category :
Languages : en
Pages : 294
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Book Description
Author: Yohan Lasorsa
Publisher: Microsoft Cloud Advocacy
ISBN:
Category : Computers
Languages : en
Pages : 35
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Book Description
Unlock the Power of Coding with Visual Studio Code! This essential guide is your key to mastering one of the most popular code editors in the world. Whether you're just starting out or looking to refine your programming skills, this book offers a step-by-step journey through the features and functionalities of Visual Studio Code. With clear explanations, practical examples, and expert tips, you'll learn how to navigate, customize, and harness the full potential of VS Code. Transform your ideas into reality and elevate your coding experience with this indispensable resource for beginners!
Author: Mathias Brandewinder
Publisher: Apress
ISBN: 1430267666
Category : Computers
Languages : en
Pages : 290
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Book Description
Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you’re new to F#, this book will give you everything you need to get started. If you’re already familiar with F#, this is your chance to put the language into action in an exciting new context. In a series of fascinating projects, you’ll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F#’s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language) Transform your data into informative features, and use them to make accurate predictions Find patterns in data when you don’t know what you’re looking for Predict numerical values using regression models Implement an intelligent game that learns how to play from experience Along the way, you’ll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.
Author: Jarred Capellman
Publisher: Packt Publishing Ltd
ISBN: 1789804299
Category : Computers
Languages : en
Pages : 287
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Book Description
Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.
Author: Mark J. Price
Publisher: Packt Publishing Ltd
ISBN: 1801076960
Category : Computers
Languages : en
Pages : 825
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Book Description
Publisher’s Note: Microsoft will stop supporting .NET 6 from November 2024. The newer 8th edition of the book is available that covers .NET 8 (end-of-life November 2026) with C# 12 and EF Core 8. Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore the newest additions to C# 10, the .NET 6 class library, and Entity Framework Core 6 Create professional websites and services with ASP.NET Core 6 and Blazor Build cross-platform apps for Windows, macOS, Linux, iOS, and Android Book DescriptionExtensively revised to accommodate all the latest features that come with C# 10 and .NET 6, this latest edition of our comprehensive guide will get you coding in C# with confidence. You’ll learn object-oriented programming, writing, testing, and debugging functions, implementing interfaces, and inheriting classes. The book covers the .NET APIs for performing tasks like managing and querying data, monitoring and improving performance, and working with the filesystem, async streams, and serialization. You’ll build and deploy cross-platform apps, such as websites and services using ASP.NET Core. Instead of distracting you with unnecessary application code, the first twelve chapters will teach you about C# language constructs and many of the .NET libraries through simple console applications. In later chapters, having mastered the basics, you’ll then build practical applications and services using ASP.NET Core, the Model-View-Controller (MVC) pattern, and Blazor.What you will learn Build rich web experiences using Blazor, Razor Pages, the Model-View-Controller (MVC) pattern, and other features of ASP.NET Core Build your own types with object-oriented programming Write, test, and debug functions Query and manipulate data using LINQ Integrate and update databases in your apps using Entity Framework Core, Microsoft SQL Server, and SQLite Build and consume powerful services using the latest technologies, including gRPC and GraphQL Build cross-platform apps using XAML Who this book is forDesigned for both beginners and C# and .NET programmers who have worked with C# in the past and want to catch up with the changes made in the past few years, this book doesn’t need you to have any C# or .NET experience. However, you should have a general understanding of programming before you jump in.
Author: Jonathan Rioux
Publisher: Simon and Schuster
ISBN: 1617297208
Category : Computers
Languages : en
Pages : 454
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Book Description
Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.In Data Analysis with Python and PySpark you will learn how to:Manage your data as it scales across multiple machines, Scale up your data programs with full confidence, Read and write data to and from a variety of sources and formats, Deal with messy data with PySpark's data manipulation functionality, Discover new data sets and perform exploratory data analysis, Build automated data pipelines that transform, summarize, and get insights from data, Troubleshoot common PySpark errors, Creating reliable long-running jobs. Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you've learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether that's Hadoop clusters, cloud data storage, or local data files. Once you've covered the fundamentals, you'll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code.
Author: Ervin Varga
Publisher: Apress
ISBN: 1484248597
Category : Computers
Languages : en
Pages : 468
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Book Description
Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is For Anyone who would like to embark into the realm of data science using Python 3.
Author: Dina Goldin
Publisher: Springer Science & Business Media
ISBN: 3540348743
Category : Computers
Languages : en
Pages : 488
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Book Description
The interaction paradigm is a new conceptualization of computational phenomena that emphasizes interaction over algorithms, reflecting the shift in technology from main-frame number-crunching to distributed intelligent networks with graphical user interfaces. The book is arranged in four sections: "Introduction", comprising three chapters that explore and summarize the fundamentals of interactive computation; "Theory" with six chapters, each discussing a specific aspect of interaction; "Applications," five chapters showing how this principle is applied in subdisciplines of computer science; and "New Directions," presenting four multidisciplinary applications. The book challenges traditional Turing machine-based answers to fundamental questions of problem solving and the scope of computation.
Author: Blaise Cronin
Publisher: Information Today, Inc.
ISBN: 9781573872096
Category : Computers
Languages : en
Pages : 712
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Book Description
ARIST, published annually since 1966, is a landmark publication within the information science community. It surveys the landscape of information science and technology, providing an analytical, authoritative, and accessible overview of recent trends and significant developments. The range of topics varies considerably, reflecting the dynamism of the discipline and the diversity of theoretical and applied perspectives. While ARIST continues to cover key topics associated with "classical" information science (e.g., bibliometrics, information retrieval), editor Blaise Cronin is selectively expanding its footprint in an effort to connect information science more tightly with cognate academic and professional communities.
