Building AI-Powered Application with .NET from Coursera

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In this comprehensive course, you'll learn how to build AI-powered applications using .NET. By exploring both foundational concepts and advanced tools, you'll gain the skills to integrate machine learning models, generative AI, and Azure AI services into .NET-based applications. You’ll also work with popular tools like ML.NET, Microsoft Copilot, and Azure Machine Learning to craft solutions that enhance your projects and workflows.

You’ll begin by exploring the evolution of AI and machine learning, covering both foundational theory and real-world applications. From there, you'll dive into the development environment, setting up Visual Studio and ML.NET to train models and integrate them into .NET applications. As you progress, you'll discover powerful tools such as Azure AI and OpenAI, leveraging these technologies to build intelligent applications, including sentiment analysis tools and image classifiers.

This course is ideal for developers who want to integrate cutting-edge AI techniques into their applications. With practical exercises on creating and deploying AI models, you'll not only understand the theory but also be able to implement AI solutions across multiple platforms.

What's inside

Syllabus

Introduction

In this module, we will introduce the course, providing an overview of how to build AI-powered applications using .NET. You’ll gain insight into the course structure, tools, and technologies you’ll learn throughout.

Career center

Learners who complete Building AI-Powered Application with .NET will develop knowledge and skills that may be useful to these careers:

AI Developer

An AI Developer creates and implements intelligent systems, integrating advanced algorithms and models into real-world applications. This foundational course is designed precisely for individuals aspiring to become an AI Developer, providing a comprehensive understanding of how to build AI-powered applications using .NET. Learners will master integrating machine learning models, generative AI, and Azure AI services. You will work with essential tools like ML.NET, Microsoft Copilot, and Azure Machine Learning, gaining practical experience in developing solutions that enhance projects and workflows. The focus on establishing a development environment with Visual Studio and deploying AI models equips you with the end-to-end skills crucial for success in this dynamic role. This course specifically enables you to craft intelligent applications, including sentiment analysis tools and image classifiers.

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Generative Artificial Intelligence Developer

A Generative Artificial Intelligence Developer specializes in building systems that create new content, such as text, images, or code, using advanced AI models. This course is exceptionally well-suited for someone pursuing a career as a Generative Artificial Intelligence Developer. It features dedicated modules on "Generative AI Tools and Copilots" and "Creating GenAI Solutions Using .NET and Azure OpenAI." You will learn to use Azure OpenAI to build intelligent chat agents, generate code with AI, and create images. The practical approach ensures you gain the skills to implement generative AI capabilities directly into .NET applications, offering a strong foundation in this cutting-edge field.

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Machine Learning Engineer

A Machine Learning Engineer is primarily responsible for designing, building, and deploying machine learning models into production systems. This course offers an exceptional pathway for those aiming to excel as a Machine Learning Engineer by focusing on practical implementation within the .NET ecosystem. You will delve into the fundamentals of machine learning, covering algorithms, training procedures, and model types. Crucially, the course guides you through setting up a development environment using ML.NET to train and integrate models into .NET applications. Furthermore, it explores Azure Machine Learning, providing the expertise needed to deploy, test, and integrate models for real-world use cases, a core competency for any aspiring Machine Learning Engineer.

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Prompt Engineer

A Prompt Engineer designs, refines, and optimizes inputs for generative AI models to achieve specific, high-quality outputs. This course is highly relevant for someone interested in becoming a Prompt Engineer, as it dives deep into "Generative AI Tools and Copilots" and "Creating GenAI Solutions Using .NET and Azure OpenAI." You will learn to leverage these tools to build intelligent chat agents, create code with AI, and generate images. This hands-on experience provides a crucial understanding of how generative AI models function and how to effectively interact with them to craft desired outputs and integrate their capabilities into .NET applications.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications across various platforms. This course significantly enhances the capabilities of an aspiring Software Engineer, particularly those looking to specialize in modern, intelligent applications. It provides a robust foundation in building AI-powered applications using .NET, a widely used framework. You will learn to integrate cutting-edge AI techniques such as machine learning models, generative AI, and Azure AI services directly into software solutions. The practical exercises on creating and deploying AI models using Visual Studio and ML.NET offer invaluable hands-on experience in augmenting traditional software engineering with powerful artificial intelligence capabilities.

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Artificial Intelligence Consultant

An Artificial Intelligence Consultant advises businesses on AI strategies, identifies opportunities for AI implementation, and guides project execution. This course provides an excellent foundation for an aspiring Artificial Intelligence Consultant. You will gain a comprehensive understanding of building AI-powered applications using .NET, exploring both foundational theory and practical tools like ML.NET, Azure AI, and OpenAI. The ability to integrate machine learning models and generative AI into real-world applications (e.g., sentiment analysis, image classifiers, chat agents) equips you to effectively recommend, design, and articulate the benefits of AI solutions to diverse clients.

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Cloud Solutions Architect

A Cloud Solutions Architect designs and oversees the implementation of cloud-based technology solutions, ensuring they meet business and technical requirements. This course is highly relevant for an aspiring Cloud Solutions Architect focusing on intelligent systems within the Microsoft Azure ecosystem. You will gain a deep understanding of integrating Azure AI Services, Azure Machine Learning, and Azure OpenAI into .NET applications. This knowledge is crucial for architecting scalable, robust, and AI-enabled cloud solutions. The practical guidance on managing Azure resources and leveraging specific AI services for content moderation, text, and image processing empowers you to design comprehensive, intelligent cloud architectures.

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Backend Developer

A Backend Developer builds and maintains the server-side logic and databases that power applications, focusing on functionality, performance, and scalability. For an aspiring Backend Developer, this course offers specialized skills in integrating advanced AI capabilities into .NET-based backend systems. You will learn how to set up the development environment, work with ML.NET to train and integrate machine learning models, and leverage Azure AI services. The curriculum covers crafting intelligent applications such as sentiment analysis tools and image classifiers, which are often backend-driven. This expertise in building robust, AI-powered APIs and services is increasingly vital for modern backend development roles.

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Applied Machine Learning Scientist

An Applied Machine Learning Scientist focuses on implementing and refining machine learning models to solve specific business or technical problems, often bridging research and engineering. While this role typically requires an advanced degree, this course offers practical skills highly valuable for an aspiring Applied Machine Learning Scientist. It covers machine learning basics, including algorithms and training procedures, and emphasizes integrating models into .NET applications using ML.NET and Azure Machine Learning. This practical application of theoretical knowledge, combined with deployment expertise, is essential for translating cutting-edge models into functional, real-world AI solutions.

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Full-Stack Developer

A Full Stack Developer manages both the front-end user interface and the back-end server logic and database for web and software applications. This course is beneficial for an aspiring Full Stack Developer seeking to integrate modern AI capabilities across their projects. You will learn to build AI-powered applications using .NET, covering both foundational concepts and advanced tools for integrating machine learning models and Azure AI services. The ability to craft practical solutions, such as sentiment analysis tools and image classifiers, allows a Full Stack Developer to create more intelligent and engaging user experiences while handling the underlying AI logic and data processing.

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Natural Language Processing Engineer

A Natural Language Processing Engineer designs and implements systems that enable computers to understand, interpret, and generate human language. This course is relevant for an aspiring Natural Language Processing Engineer, explicitly covering the creation of "sentiment analysis tools" and working with "text processing tools" using Azure AI Cognitive Services. Furthermore, the modules on generative AI with Azure OpenAI include building "intelligent chat agents." This practical experience in developing language-aware AI solutions within .NET applications offers valuable skills for understanding and implementing various Natural Language Processing tasks.

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Computer Vision Engineer

A Computer Vision Engineer develops systems that enable computers to "see" and interpret visual data, such as images and video. This course is relevant for an aspiring Computer Vision Engineer, as it explicitly addresses building "image classifiers" and working with "image processing tools" using Azure AI Cognitive Services. The practical exercises in creating and deploying AI models, specifically those processing visual information within .NET applications, provide a solid foundation. This allows you to understand how to integrate advanced visual intelligence capabilities into applications, which is a core task for a Computer Vision Engineer.

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DevOps Engineer

A DevOps Engineer focuses on streamlining the software development lifecycle, from integration and delivery to deployment and operations. This course may be helpful for an aspiring DevOps Engineer, particularly one involved in AI-powered applications. You will learn about deploying and testing machine learning models and integrating them into .NET applications, along with guidance on managing Azure resources. This understanding of the AI application deployment pipeline and the specific requirements for scaling and monitoring AI services like Azure Machine Learning and Azure AI is valuable for ensuring robust, efficient, and reliable operations of intelligent systems.

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Data Scientist

A Data Scientist analyzes complex datasets to extract insights and build predictive models, often contributing to research and algorithm development. While this role typically requires an advanced degree, this course may be useful for an aspiring Data Scientist by providing practical experience in implementing machine learning models within an application context. You will explore the evolution of AI and machine learning, foundational theory, and machine learning basics, including algorithms and training procedures. This understanding of how models are created and integrated into .NET applications can complement a Data Scientist's analytical skills, bridging the gap between model development and practical deployment.

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Edge Artificial Intelligence Developer

An Edge Artificial Intelligence Developer builds and deploys AI models optimized to run on local devices rather than in the cloud, enabling real-time processing and reduced latency. This course may be helpful for an aspiring Edge Artificial Intelligence Developer by providing core competencies in building and deploying AI models. While it focuses on cloud services like Azure AI, the fundamental principles of training models with ML.NET and integrating them into .NET applications are transferable. Understanding how to structure AI-powered applications and make them efficient is valuable, even if specific edge deployment tools are not explicitly covered.

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