Course duration: 1,79h Dans cette formation d'Omar Souissi, vous allez voir comment associer le langage Python et l'agent conversationnel AI ChatGPT pour le développement. Après avoir passé en revue l'intérêt des différents composants employés ici et installé l'environnement de travail, vous commencerez par l'analyse de données et la mise en œuvre du machine learning, du deep learning et du traitement automatique naturel du langage (NLP). Vous verrez comment créer, entre autres, des applications, faire du web scraping, automatiser la rédaction de messages, etc. Tout au long de cette formation, vous mettrez également en œuvre de nombreuses librairies. Topics include:   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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:   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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.   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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.   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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.   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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:   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

Course duration: 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.   This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

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: Deze cursus is enkel beschikbaar in het Engels. Als dit voor u geen probleem vormt, dien dan gerust uw aanvraag in. This course is in French only. If this is not a problem for you, by all means go ahead and apply. Apply for this course

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.

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.

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

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.

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.

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.