Artificial Intelligence Information Resources

Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Durée de la formation : 1,11h The next generation of GPT is here, and it’s time to get up to speed. The latest version, GPT-4 Turbo, takes all the core functionalities of GPT-4 and reworks them for more reliable, consistent results and higher performance delivered at speed. Join AI expert Jonathan Fernandes to get a sneak peek at what’s new with the latest release, in this approachable, easy-to-follow course designed for both technical and nontechnical learners. Explore the wide variety of tasks that GPT-4 Turbo can perform with accuracy and ease, as you develop your understanding of how large language models function in interactive natural language processing. Along the way, gather insights from Jonathan on some of the most talked-about updates such as the Assistants API, Code Interpreter, the retrieval tool, function calling, GPTs, and more. Upon completing this course, you’ll have the skills you need to know to get started with GPT-4 Turbo and select the model that’s best for you. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast. This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI. Topics include: The history of AI Machine learning Technical approaches to AI AI in robotics Integrating AI with big data Avoiding pitfalls Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Durée de la formation : 1,19h AI is revolutionizing the way project managers work, offering unprecedented opportunities to enhance efficiency, accuracy, and innovation. In this course, instructor Oliver Yarbrough simplifies the complexities of artificial intelligence and shares practical ways you can use AI in your projects. Topics include: Explain how AI is transforming the project management profession. Describe the importance of integrating human strengths with AI capabilities. Build an AI project model and discuss specific use cases for artificial intelligence. Explain how AI can be used to monitor projects in real-time and forecast more accurately. Walk through how to derive value from data with AI and apply it to projects. Select AI tools that best meet the needs of specific project scenarios. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 2,45h Computer scientists are just a small slice of people working in artificial intelligence (AI). Most people working with AI are just like you. They’re professionals, teachers, and students who want to use AI to enhance their products, creativity, and career. AI has been around for over half a century. Despite huge advancements in predictive and generative AI, the core concepts of artificial intelligence are still accessible. This course is designed for project managers, product managers, directors, executives, and students starting a career in AI. First, learn what it means for a system to display “intelligence.” Then, explore the difference between classic predictive AI and newer generative AI. Next, you’ll get an overview of machine learning algorithms, artificial neural networks, foundation models, and deep learning. From the AI curious to the AI careerist, this course will help you get started with intelligent systems. This course is part of a Professional Certificate from Microsoft. This course is part of a Professional Certificate from Microsoft. Topics include: List the distinctions between symbolic systems and machine learning. Identify challenges in natural language processing. Define the various types of machine learning. Explain the importance of algorithms in machine learning. Determine conditions in which using artificial intelligence is appropriate. Review the differences between artificial intelligence and machine learning that impact business decisions. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.

Artificial intelligence (AI) offers businesses the potential for a dramatic increase in functionality and profitability, but it can also spark an array of complex ethical, legal, and social challenges. In this nontechnical, conceptually oriented course, Barton Poulson digs into the hazards of AI, offering potential solutions to key concerns. Barton explores the ethical issues posed by AI, including competing concepts of fairness and moral reasoning. He also goes over social concerns and safety challenges for AI, such as potential life-and-death scenarios in autonomous driving. Barton concludes with recommendations tailored to developers, executives, PR professionals, regulators, and consumers to help them reap the potential of AI in a manner that's worthy of trust and profitable to all. Topics include: Review the challenges of AI. Apply narrow AI to a decision. Define two major approaches used when dealing with AI. Examine supervised and unsupervised learning. Explain harassment by AI. Identify three concepts that distributive justice is based on. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Durée de la formation : 0,65h In this course, learn to stop feeling anxious about AI and how to effectively integrate AI skills into your daily routine. Instructor Amy Blankson starts by acknowledging common concerns about AI and shows you how to view it as a valuable tool rather than a threat. The course encourages you to explore various AI tools with practical applications, solve real-life problems, and address challenges with structured problem-solving techniques. Additionally, discover ways to infuse empathy into your AI outputs, work more efficiently, and ethically apply AI in your endeavors. Topics include: Identify and acknowledge common anxieties about AI and how to address them. Explore and utilize AI tools to solve real-life challenges and improve productivity. Apply structured problem-solving techniques to overcome AI-related challenges. Integrate ethical guidelines and empathy into AI-generated outputs. Develop strategies to seamlessly incorporate AI into everyday routines for maximum efficiency. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. The course will use C# or Python as the programming language.Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.

Explore the fundamentals of GitHub Copilot and its potential to enhance productivity and foster innovation for both individual developers and businesses. Discover how to implement it within your organization and unleash its power for your own projects. In this learning path, you'll: Gain a comprehensive understanding of the distinctions between GitHub Copilot Individuals, GitHub Copilot Business, and GitHub Copilot Enterprise. Understand how to utilize GitHub Copilot across various environments responsibly and securely. Learn advanced functionalities of GitHub Copilot and how to best use them. Prerequisites Basic understanding of GitHub fundamentals

Durée de la formation : 1,94h If you’re looking to keep up with the rapid advancements and applications of deep learning techniques, this course provides a comprehensive guide that can help you stay relevant and competitive in the evolving landscape of AI and data-driven technologies. Instructor Gwendolyn Stripling shows you how to transform raw data into valuable insights and build the foundation for cutting-edge AI applications. The course focuses on the concepts, with minimal coding required, so even if you’re not an experienced coder, Gwendolyn shows you how to use simple Python code to work with data. Test your learning with a series of challenges, and cap off the course with building and evaluating a predictive and generative model. Topics include: Identify common applications of deep learning and generative AI in various fields such as computer vision, natural language processing, and healthcare. Evaluate the quality of a dataset and make informed decisions about data preprocessing strategies based on factors such as data distribution, imbalance, and outliers. Understand data preprocessing, cleaning, transformation, exploratory data analysis, feature engineering, and data augmentation in training effective generative AI models. Distinguish between the goals of predictive AI and generative AI, understand the methodologies employed in each paradigm, and identify the unique outputs generated by predictive models versus generative models. Create data visualizations using Python libraries like Matplotlib and Seaborn, depicting data distributions, trends, and relationships. Explore data analysis techniques, such as statistical analysis and visualizations, to structured and unstructured data to understand data distributions, identify outliers, and detect correlations. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 0,56h Looking to boost your understanding of agentic AI? This course was designed for you. Join instructor and AI expert Zach Kass as he explores the impact of agentic AI on businesses and teams, sharing real-world case studies and insights on practical applications, ethical considerations, and more. Discover how intelligent agents are designed, as well as their historical development, underlying mechanisms, and core functionalities. Along the way, Zack analyzes the current and future role of agentic AI in a wide variety of business roles across different organizational levels and industries. Upon completing this course, you’ll be equipped with a solid understanding of agentic AI and its potential applications across your organization. Topics include: Define agentic AI and distinguish it from other forms of AI. Explain the key mechanisms of agentic AI, including perception, decision-making, autonomous action, and learning and adaptation. Analyze examples of agentic AI applications in various industries such as autonomous vehicles, healthcare, retail, and finance. Evaluate the ethical concerns associated with agentic AI, including undue semantics, blast radius, and the principle of least privilege. Describe strategies for integrating agentic AI into business operations, focusing on assessing business needs, building adaptive AI strategies, and engaging and training teams. Identify the potential future advancements of agentic AI and the challenges these developments might present. Apply knowledge of AI ethics to ensure responsible deployment and use of agentic AI systems. Outline the benefits of agentic AI in enhancing efficiency, productivity, and team dynamics within organizations. Predict the impact of agentic AI on job roles and workforce dynamics, and formulate approaches to manage workforce displacement. Summarize practical techniques for balancing innovation with practicality in AI adoption. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 1,75h Cutting-edge artificial intelligence technologies are changing the world. But without proper deployment and management, your applications may never reach their full potential. Worse, they could simply fail or even cause critical errors in your systems. As more organizations are incorporating large language models into their workflows, there's an increasing need for professionals skilled in deploying and monitoring these models effectively, responsibly, and securely in production environments. In this course, learn the advanced techniques and best practices for deploying and monitoring LLMs in production environments. Explore LLM deployment options, handling API limitations, performance monitoring techniques, prompt management, addressing hallucinations, and more. Plus, learn about security and cost considerations, and test your learning with challenges and solutions. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

This module provides engineers with the skills to begin building an Azure OpenAI Service solution. Learning objectives By the end of this module, you'll be able to: Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models. Use the Azure AI Studio, console, or REST API to deploy a base model and test it in the Studio's playgrounds. Generate completions to prompts and begin to manage model parameters. Prerequisites Familiarity with Azure and the Azure portal. An understanding of generative AI.

Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models with prompt flow to provide value to your users. Prerequisites Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.

Ever wondered what’s really going on underneath a machine learning algorithm? The answer is linear algebra. Without it, machine learning can’t exist. Linear algebra is a prerequisite for understanding and creating nearly all machine learning algorithms, especially those that prop up neural networks, natural language processing tools, and deep learning models. Join instructor Terezija Semenski for an in-depth exploration of the core concepts of linear algebra alongside the techniques needed to design and implement a successful machine learning algorithm. Discover the basics of vector arithmetic, vector norms, matrix properties, advanced operations, matrix transformation, and algorithms like Google PageRank. By the end of this course, you’ll be ready to take the principles of linear algebra and apply them to your next big machine learning project. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Image processing is a ubiquitous technology these days, used in just about every feature of smartphone cameras, in video games, social media apps, graphics software, and much more. Computer vision is the field of science that makes it possible for computers to not just capture images, but to process, analyze, and understand digital images. In this course, Eduardo Corpeño shows you how to use Python to write image processing operations of your own from scratch, to get a deeper understanding of the under-the-hood operations of image processing and popular tools like OpenCV. Eduardo shows you how to tap into the details of the algorithms behind image processing so you can understand what’s going on inside and make better use of these tools in the future. He also provides programming challenges so you can create your own implementations and compare your work with his solutions. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Durée de la formation : 1,26h AI is driving innovation and efficiency in the tech industry. As businesses and organizations seek to leverage AI, there's a high demand for skilled professionals who can understand, develop, and ethically implement AI technologies. In this course, award-winning tech innovator and AI/ML leader Kesha Williams helps developers to upskill and merge their existing programming knowledge with AI competencies. Learn about the concept of artificial intelligence and how it revolutionizes traditional programming methodologies. Explore the tools you need to interpret, evaluate, and harness AI technologies effectively. Through Python code examples, get an introduction to the fundamental pillars of AI, including machine learning, neural networks, and computer vision, while addressing ethical considerations for responsible development. By the end of the course, you will be ready to tackle the technological challenges of today and tomorrow with confidence and creativity. Topics include: Upskill existing programming knowledge with AI competencies. Interpret and evaluate AI technologies and their applications. Gain hands-on experience with building, testing, and debugging AI models. Learn practical Python programming skills for developing AI solutions. Explore the use of key Python libraries essential for AI development. Understand the ethical considerations and best practices in AI development. Integrate AI with traditional programming approaches. Inspire innovation and creativity in applying AI to solve real-world problems. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 1,38h The European Union (EU) Artificial Intelligence (AI) Act is a first-of-its-kind global regulation for trustworthy AI. Once passed into law, the act will enforce a legal framework using a risk-based approach aimed at regulating the development, deployment, and use of AI in the European Union. Companies failing to abide by the reporting and transparency requirements defined in the act will be subject to monetary repercussions. In this course, Tristan Ingold examines the transformative EU AI Act. He defines AI systems in the context of the EU and identifies requirements and obligations imposed by the act on AI system providers and users. He also details the various business and technical challenges and opportunities that stakeholders will have to consider. Whether you are a developer, business leaders, risk and compliance professional, or just interested in understanding the future of AI regulation, this course should help you understand how legislative developments will impact AI systems. Topics include: Understand how, when, and on which companies the EU AI Act will be enforced once it is ratified. Define AI in the context of the EU and identify AI systems at the various risk levels. Identify the various user and system obligations imposed by the AI Act. Apply the principles of the AI Act in real-world scenarios to promote compliance while encouraging innovation and ethical AI development. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 0,39h As AI adoption in organizations evolves, managers need to ensure their teams are using AI responsibly. In this course, instructor Terri Horton shows you how to design and implement responsible AI practices and hold yourself and your team accountable as a manager. Explore the manager's role in responsible AI, in theory and in practice. Get an overview of the guiding principles of ethical AI and responsible AI in management. Terri offers insights on accountability—including data privacy, safety, and how to mitigate bias and risk—before covering key management practices during AI implementation such as decision-making, employee engagement, and psychological safety. Along the way, learn more about AI in action with real-world case examples drawn from business management. This course is part of a Professional Certificate from LinkedIn Learning. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Innovations in finance, health, robotics, and a variety of other sectors have been made possible with reinforcement learning (RL), which involves the training of machines to learn from their environment. Many top tech companies are investing heavily in this field. In this course, instructor Khaulat Abdulhakeem helps you learn the basics of this relatively new, but valuable skill. Get to know the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. Khaulat shows you how to define and represent reinforcement learning problems. She also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, she explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Durée de la formation : 0,67h Keen on leveraging AI for design, yet apprehensive about unintended consequences? Jasmine Orange understands how you feel. Join her in this course and explore human-centered AI design principles and ethical AI considerations. Learn about core principles and related fundamentals like accountability, explainability, transparency, fairness, data rights, and privacy. Find out how to apply ethical design standards in real-world scenarios. Discover the steps involved in Ben Schnidermann's human-centered UI process. Go through the processes of mapping user journeys that leverage AI-centric interactions and prototyping AI-centered experiences. Along the way, Jasmine demonstrates how ethical guidelines can be incorporated into design workflows. Whether you are a designer looking to refine your approach to AI projects, or a tech enthusiast who wants to learn about thoughtful uses of AI, the insights in this course can help you to embrace and foster responsible design practices. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 1,79h Python is a versatile programming language that is widely used in a variety of industries, including data science, artificial intelligence, web development, and more. As the demand for Python developers continues to grow, having a portfolio of Python projects can significantly increase your job prospects and marketability. This course with instructor Priya Mohan is designed to equip you with the skills and knowledge needed to create a portfolio of Python-based applications and tools that can be showcased to employers or used to bring your own ideas to life. It’s ideal for anyone looking to enhance their Python knowledge by completing hands-on projects or for those seeking to create interesting solutions from scratch for fun. This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 1,29h Large language models (LLMs) have taken the AI world by storm. LLMs are behind some of the biggest AI technologies over the last few years, like ChatGPT and GPT-4. In this course, Jonathan Fernandes provides an overview of LLMs suitable for technical learners and non-technical learners alike. Jonathan shows you what LLMs are and what you can do with them, and takes a look under the hood so you can understand why they work the way they do and how they can affect your work. He explains how LLMs are trained and details the components of LLMs, and then takes a look at several different applications of LLMs—including Google’s BERT, GPT-3, PaLM and PaLM 2, ChatGPT and GPT-4, and Llama—and shows you how to compare LLMs using benchmarks. Topics include: Describe the architecture of large language models and their components such as transformers, encoders, and decoders. Explain the function and importance of model parameters in neural networks. Analyze the efficiency and effectiveness of different language models like GPT-3, BERT, and PaLM in various tasks. Interpret the role of scaling laws in optimizing large language models' performance. Evaluate the impact of model size and training data volume on the effectiveness of language models. Identify the benefits and drawbacks of open community language models compared to proprietary models. Compare language model performance using benchmarks like HELM and understand their implications. Discuss the trends in reducing inference costs and improving model performance. Illustrate the concept of tokenization and context windows in the generation of responses by language models. Outline the key challenges and limitations of large language models, including their inability to update in real-time. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Do you know how AI and machine learning affect you, your career path, and the world around us? This learning path teaches you the concepts and future directions of artificial intelligence and machine learning. You'll be able to make more informed decisions and contributions in your work environment. Gain an understanding of how AI and machine learning work. Discover how leading companies are using AI and ML. Learn how AI addresses accountability, security, and more. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Demande de formation

Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications. Before starting this learning path, you should already have: Familiarity with Azure and the Azure portal. Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path before starting this one.

Durée de la formation : 0,82h Explore the dynamic landscape of artificial intelligence and gain a strong understanding of both traditional and generative AI. Join instructor and generative AI expert Dr. Marily Nika as she offers an overview of the history of AI—from its conceptual inception to its current state. Discover the differences between traditional AI and generative AI, before diving into constructing effective prompts to enhance the usefulness of AI responses in various contexts. Learn about key technologies including transformer architecture, large language models (LLMs), and real-world applications like chatbots, translators, and agents. Along the way, Marily covers the importance of data quality, expert human contribution, and the back-end technology, such as GPUs, all of which are essential for training AI systems. This course is an ideal fit for tech enthusiasts, data scientists, professionals in tech-driven fields, and anyone looking to leverage AI for personal or professional growth. Topics include: Compare traditional AI systems with generative AI models and describe their unique capabilities and applications. Explain what a GPU is and how it leads to overall cost for running AI models. Outline the key components of an effective generative AI prompt. Identify the ethical considerations in AI development and explain strategies for practicing responsible AI. Summarize the impact of data quality on AI outcomes. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

Durée de la formation : 1,06h Learn how to craft high-impact prompts for generative AI tools. Whether you’re a beginner or have been experimenting with generative AI tools, this course offers a great introduction to the art of prompt engineering. Senior AI engineer Ronnie Sheer guides you through what large language models are and what problems they may be able to solve. Ronnie dives into text generation, starting with a warning to use text generation AI responsibly, then moving on to Copilot, ChatGPT, Gemini, and Claude. He introduces you to the AI generated image landscape, then shows you how to use Dall-E and Midjourney. Plus, Ronnie goes over advanced concepts like fine-tuning your prompts and interacting with language models using an API. This course is part of a Professional Certificate from Microsoft. Topics include: Describe the concept of prompt engineering and its importance in effective interaction with generative AI systems. Use generative AI tools like ChatGPT, Google Gemini, Microsoft Copilot, and Anthropic's Claude to generate text and images for different applications. Explain the basic components of AI tools, including tokens in the context of generative AI. Evaluate the capabilities and limitations of language models to determine which problems can be solved by them. Demonstrate awareness of ethical and security considerations in responsible AI use. Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation

In this learning path, discover how Azure AI Document Intelligence solutions can enable you to capture data from typed or hand-written forms. Learn how to build a solution for your custom form types and integrate that solution into an Azure Cognitive Search pipeline. You'll learn how to: Design a solution that analyzes your business forms by using Azure AI Document Intelligence. Create a solution that analyzes common documents by using Document Intelligence. Create a solution that analyses different custom form types by using Document Intelligence. Include an Azure AI Document Intelligence service as a custom skill in an Azure AI Search pipeline.

Durée de la formation : 1,72h As generative AI models have become increasingly popular, enterprises have started to build end-to-end applications to integrate their existing workflows with generative AI. In this course, instructor Kumaran Ponnambalam shows you how to get up and running with integration, performance management, trust, and monitoring to deliver effective and trustworthy generative AI applications at scale. Explore some of the unique characteristics and use cases for generative AI-powered applications in an enterprise setting, including available options, selection criteria, and key deployment considerations for generative AI models. Kumaran covers the basics of evaluating and fine-tuning models as well as patterns and best practices for core application design. By the end of this course, you’ll also be equipped with new skills to manage application performance, maintain safety and trust, and navigate some of the most important ethical and legal challenges of AI. Topics include: Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.   Demande de formation