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.
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Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.
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.
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.
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.
Artificial Intelligence Information Resources
- Enseignant: Katy Lucie Fokou Tchomtchoua
- Enseignant: Joachim Ganseman
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.
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.
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.
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.
This course is suitable for IT personnel who are just beginning to work with Azure. This audience wants to learn about our offerings and get hands-on experience with the product. This course primarily uses the Azure portal and command line interface to create resources and does not require scripting skills. Students in this course will gain confidence to take other role-based courses and certifications, such as Azure Administrator. This course combines lecture, demonstrations, and hands-on labs. This course will also help prepare someone for the AZ-900 exam.
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.
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Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.
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.
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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.
There is a growing demand to harness the power of natural language processing (NLP) and deep learning models to be able to make sense of textual data and reduce the emotional intervention of humans in order to make better decisions. In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs). Harshit begins by introducing you to word encodings and using TensorFlow for tokenization. He describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. Harshit discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course. He concludes with a discussion of how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text.
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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.
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.
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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.
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.
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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.
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.
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.
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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.
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.
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Gain an understanding of how AI and machine learning work.
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Discover how leading companies are using AI and ML.
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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.