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This course introduces fundamental concepts related to artificial intelligence (AI) and services in Microsoft Azure that can be used to build AI solutions. The course is not designed to teach students how 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 course is designed as a blended learning experience. The hands-on exercises in the course are based on Learning Modules, and students are encouraged to use the content as reference materials to reinforce what they learn in class and explore the topics in greater depth. The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solutions that artificial intelligence (AI) makes possible and the services in Microsoft Azure that you can use to build them. You don't need to have any experience using Microsoft Azure before taking this course, but a basic level of familiarity with computing 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 graphs. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. The course is based entirely on the official Microsoft AI-900 - Microsoft Azure AI Fundamentals material . I hope you can pass the exam with the different aids that I will present to you. The course includes PDF study material for the exam. I wish you much success! THIS COURSE IS FOR EXCLUSIVE USE ON THE UDEMY PLATFORM. ITS COMMERCIALIZATION IS PROHIBITED.

Embark on a transformative learning journey designed to elevate your expertise from a Deep Learning Engineer to a globally recognized AI Researcher. This comprehensive course is meticulously crafted to provide you with the skills, knowledge, and strategic mindset essential for making a significant impact on the forefront of artificial intelligence research. Deep Learning Fundamentals Review Refresh your understanding of core deep learning concepts, frameworks, and model architectures. Dive into advanced topics, including neural network architectures, optimization techniques, and transfer learning. Expanding into Broader AI Domains Broaden your expertise by exploring diverse AI domains such as natural language processing, computer vision, reinforcement learning, and robotics. Analyze real-world applications and challenges in each domain to foster a comprehensive understanding. Strengthening Mathematical Foundations Deepen your mathematical knowledge with a focus on linear algebra, calculus, probability, and statistics. Apply advanced mathematical concepts to analyze, optimize, and innovate within machine learning algorithms. Pursuing Advanced Education in AI Consider advanced education options such as pursuing a master's or Ph.D. in AI or a related field. Engage with leading researchers, attend conferences, and explore opportunities for research collaboration. Engaging in Research Projects Participate in hands-on research projects to apply theoretical knowledge to real-world scenarios. Collaborate with experienced researchers and mentors to develop practical research skills. Navigating Research Methodologies Master effective research methodologies specific to AI, including experimental design, data collection, and analysis. Formulate impactful research questions and hypotheses to drive innovative projects. Building a Strong Research Portfolio Craft a comprehensive research portfolio showcasing your achievements, projects, and contributions. Learn the art of effective communication to present research findings to both technical and non-technical audiences. Specialization in Niche Areas Identify a specific niche within AI that aligns with your passion and strengths. Develop deep expertise in your chosen area, contributing unique insights to advance the field. Continuous Learning and Innovation Embrace a culture of continuous learning to stay updated on the latest AI research trends. Foster innovation by exploring novel ideas, experimenting with new approaches, and pushing the boundaries of existing knowledge. Recognition and Contribution to AI Community Strategically position yourself for recognition within the AI community through conference presentations, awards, and active participation. Give back to the community by mentoring aspiring researchers and contributing to educational initiatives. Key Takeaways Mastery of advanced AI concepts, methodologies, and mathematical foundations. Hands-on experience in executing impactful research projects. Strategic networking and collaboration skills within the global AI community. Expertise in niche areas of AI research. Graduation with a robust research portfolio and recognition as a top-tier AI researcher. Who Should Enroll Deep Learning Engineers aiming to transition into AI research and make substantial contributions to the field. Experienced professionals seeking to elevate their careers by becoming globally recognized AI researchers. Enroll now in "Mastering the Transition" and take the definitive step towards making a lasting impact on the forefront of artificial intelligence research. Unleash your potential and join a community dedicated to shaping the future of AI research! Courtesy, Dr. FAK Noble Ai Researcher, Scientists, Product Developer, Innovator & Pure Consciousness Expert Founder of Noble Transformation Hub TM

The "Ethical AI Use" course delves into the critical intersection of technology and ethics, focusing on the responsible development and deployment of artificial intelligence (AI). Participants will explore the evolution of AI, distinguishing between the two fundamental types, and examine the ethical considerations inherent in the realm of generative AI. Through a combination of theoretical discussions and practical exercises, students will learn about the intricate process of prompt engineering, which involves crafting inputs to guide AI systems towards ethically sound outputs. They will investigate the four qualifiers that govern AI output and master the techniques of proper prompting to ensure adherence to ethical standards. Drawing from real-world examples, including case studies centered on images, participants will analyze the ethical implications of AI applications and develop a comprehensive understanding of the ethical wheel guiding prompt engineering decisions. Ultimately, students will come to appreciate prompt engineering as both a skill and an art, essential for the ethical use of AI in various domains. This course equips participants with the knowledge and tools necessary to navigate the ethical complexities of AI, empowering them to contribute positively to the development and implementation of AI technologies in society.

This course equips learners with the essential knowledge and practical tools to develop and implement artificial intelligence (AI) responsibly. Through an exploration of ethical considerations, best practices, and governance procedures, participants will gain an understanding of how to navigate the complex landscape of AI while upholding ethical standards and minimizing potential risks.

As generative artificial intelligence (AI) reshapes our world, the ability to analyze data is quickly becoming as fundamental as reading and writing. “AI-Powered Data Analysis: A Practical Introduction” explores how AI tools like ChatGPT are revolutionizing our approach to data, making advanced analysis accessible to everyone. Whether you're a complete novice or looking to enhance your skills, you'll learn how to navigate this new terrain. You'll learn to think critically about the context of data analysis, delve into the specifics of analyzing and visualizing data using AI, and consider broader factors that support but are not directly part of data analysis. This practical approach focuses on generative AI tools, ensuring you know how to ask the right questions to avoid common mistakes. Your final activity will allow you to set yourself up for continued learning with a prepared Python environment and data sets, which you can voluntarily showcase on GitHub—a code-sharing hub. By the end of this course, you'll be adept at using AI tools to analyze data effectively and seamlessly apply these skills to future projects.

This course helps you understand some common issues of around security, compliance, and governance associated with artificial intelligence (AI) solutions. You will learn how to recognize governance and compliance requirements for AI systems. You will also learn about various Amazon Web Services (AWS) services and features that will help you apply governance controls and achieve your compliance objectives. Finally, you will be introduced to AWS services that can help you secure your AI systems.

In this course, you will explore the generative artificial intelligence (generative AI) application lifecycle, which includes the following: - Defining a business use case - Selecting a foundation model (FM) - Improving the performance of an FM - Evaluating the performance of an FM - Deployment and its impact on business objectives This course is a primer to generative AI courses, which dive deeper into concepts related to customizing an FM using prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning.

In this course, you will learn about responsible artificial intelligence (AI) practices. In the first section of this course, you will be introduced to what responsible AI is. You will learn how to define responsible AI, understand the challenges that responsible AI attempts to overcome, and explore the core dimensions of responsible AI. Then in the next section of the course, you will dive into some topics for developing responsible AI systems. In this section of the course, you will learn about some of the services and tools that AWS offers to help you with responsible AI. You will also learn about responsible AI considerations for selecting a model and preparing data for your AI systems. Finally, in the last section of the course, you learn what it means for a model to be transparent and explainable. You will also learn about tradeoffs to consider between safety and transparency for an AI model and the principles of human-centered design for explainable AI.

In this course, you will explore real-world use cases in artificial intelligence (AI), machine learning (ML), and generative artificial intelligence (generative AI) across a range of industries. These areas include healthcare, finance, marketing, entertainment, and more. You will also learn about AI, ML, and generative AI capabilities and limitations, model selection techniques, and key business metrics.

In this course, you will learn about the foundations of machine learning (ML) and artificial intelligence (AI). You will explore the connections between AI, ML, deep learning, and the emerging field of generative artificial intelligence (generative AI), which has captured the attention of businesses and individuals alike. You will gain a solid understanding of fundamental AI terms, laying the groundwork for a deeper dive into these concepts. Additionally, you will learn about a selection of Amazon Web Services (AWS) services that use AI and ML capabilities. You will gain practical insights into how these tools can be used to solve real-world problems and drive innovation across various industries.

Generative AI is a disruptive technology with enormous impact. It is forcing companies to rethink their customer strategies and redesign their products, chip makers are struggling to keep up with increased demand for processors, and in academia, educators are changing curricula and programs across all disciplines. The material presented in this course will introduce you to the world of Generative AI and uncover some of the ingredients of this groundbreaking field. Using basic terms and a few simple examples, we will explain what Generative AI is, what makes it work, and help you lay the foundation for the next step in your learning journey. Throughout the course, you will learn about big language models, neural networks, training datasets, and Prompt. You will also be introduced to some of the tools and processes used to build Generative AI applications. This course is designed for students, early career professionals, and anyone new to Machine Learning who wants to learn more about Generative AI. The material is "code-free" and lays a foundation of knowledge that can be used to study Generative AI and Machine Learning in depth.

AI is the hottest, most exciting technology to burst onto the scene in recent years. Its impact is so big, it is revolutionizing industries all over the globe. If you are an executive, you may be wondering how to harness AI's incredible power for your organization. Well, this course is going to show you how to do just that. This Short Course was created to help leaders in organizations harness the power of AI to create long-term value for their businesses. By completing this course you'll be able to develop and execute an effective AI Strategy for your organization that enables efficiency, growth, and an increased competitive advantage. More specifically, in this 1.5-hour long course, you will learn how to develop your organization's GenAI capability to enable the sustainable delivery of AI-powered solutions, analyze the potential applications of GenAI in your organization, identify opportunities for improvement and expansion, and apply a governance structure that assures the implementation of GenAI solutions that are ethical and compliant with legislation. This course is unique because it will give you all the essentials you will need to turn your organization into an AI-fueled powerhouse.