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This short course introduces cybersecurity professionals and enthusiasts to the latest Generative AI tools to solve complex cybersecurity problems. The course focuses on combating the exploitation of undetected vulnerabilities, for which organizations are increasingly turning to Artificial Intelligence (AI) and Machine Learning (ML). Generative AI, a transformative technology, is becoming a critical cybersecurity tool that enables the detection and prevention of attacks by identifying and neutralizing unknown vulnerabilities before they cause significant damage. The course explores the fundamental principles of Generative AI and its application in real-world cybersecurity, covering User and Entity Behavior Analytics (UEBA), threat intelligence, report summarization, playbooks, and their impact on phishing, malware, disinformation, and deep fakes. In addition, participants will learn about potential Natural Language Processing (NLP) attack methods such as Prompt injection and their mitigation strategies. The course allows attendees to stay up-to-date with the latest trends in the use of Generative AI for cyber defense and threat prediction. Throughout the course, you will be able to apply your knowledge in hands-on activities. In addition, real-world-inspired exercises will help you apply your knowledge in practice. This course is suitable for both current cybersecurity professionals who want to advance their professional skills and those who aspire to start a career in the field, such as cybersecurity specialists, cybersecurity analysts, cybersecurity engineers, etc. Cybersecurity skills and basic knowledge of Generative AI are useful for this course.

This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

Any business professional or team in an organization is interested in learning about artificial intelligence, machine learning, and Google Cloud technology.

This is aimed at absolutely anyone interested in AI, from C-suite and software engineers to curious parents and engaged amateurs. No technical background is necessary, as no matter your background you will leave much better informed about key (and controversial) terms in AI. You will learn about 8 of the key terms used in AI - which are bandied around as if everyone should know them. Each term comes with complex and storied histories and implications, relevant to both practitioners and interested onlookers. What does 'stochastic parrot' actually mean, and are LLMs merely parrots? Do language models actually 'reason' and has the idea of ​​'emergent behaviors' been debunked already? Why is the term 'open source' being replaced with 'open weights' and what on earth is meant by 'artificial general intelligence'? I challenge anyone to leave this short 3-5 hour course without a much more nuanced understanding of the key debates in AI.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use a LangChain "Chain" to orchestrate the steps required to query a vector database and submit the results of the query to Gemini to obtain results based on a knowledge base. To do this, you embed a query submitted in a session in order to perform a nearest neighbor search on the vector store. Once the results are retrieved, they are added as contextual information when querying a large language model (LLM), in this case, Gemini.

Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionize machine learning solutions using Vertex AI.

Learning Outcomes: This introductory course combines theoretical knowledge with practical ideas for real-world business scenarios, making it ideal for those looking to integrate AI into a social ethics strategy. By the end of the course, you will be able to: - Understand the fundamentals of AI - Explore the ethical aspects - Understand the regulatory environment - Address privacy and fairness issues - Practical Takeaways This course covers intermediate knowledge of AI technology and its current applications in business. It also covers foundational topics in ethics and the regulatory framework as they relate to AI issues. In addition, the course provides real-world case studies and predictions for the future. This course combines AI technologies with business applications and social ethics to provide a unique and comprehensive learning experience. Participants will gain practical skills to responsibly implement AI solutions and comply with regulatory requirements. The course connects theoretical concepts with real-world business and ethical scenarios through case studies and real-world examples. Discussions of future AI trends and ethical dilemmas will help students become leaders in their fields. This intermediate-level course is designed for students and professionals in a variety of fields, including business managers, IT professionals, policymakers, and those seeking to implement AI in their careers. To successfully complete this course, you will need an understanding of fundamental business operations and basic knowledge of AI (common AI applications in business and core algorithms).

Explore the boundaries of technology, predict the future, and thrive in an ever-changing world. Our immersive course combines cutting-edge ideas, expert advice, and creative vision to help you become a disruptor, not a bystander. From evolving technologies and AI to storytelling and speculative design, gain the knowledge and skills to shape tomorrow’s possibilities. Embrace innovation, anticipate challenges, and chart your path in an era of disruption. Join us now and unlock your potential to become a leader and innovator in a dynamic digital landscape.

This course begins with an examination of the social context and purposes of literacy teaching and learning. It then describes a range of historical and contemporary approaches to literacy pedagogy, including didactic, authentic, functional, and critical approaches. The course takes a 'Multiliteracies' approach, which seeks to broaden the definition of literacy to encompass contemporary multimodal communication and the diversity of literacies in different social and cultural contexts. Multiliteracies also involves a wide range of activities - experiential, conceptual, analytical, and critical. Suggested Reading This course is designed for people interested in literacy teaching and learning, including those who want to become education professionals, practicing teachers interested in exploring the future directions of their profession, which is currently undergoing change, and community and workplace leaders who see their mission as partly “educational.” Related Resources Take this course for credit at the University of Illinois This course has the same content and requires the same level of student input as the Assessing Learning Outcomes course offered to graduate, master's, and doctoral students in the Learning Design and Leadership program in the College of Education at the University of Illinois. Of course, due to the nature of MOOCs, many people will just want to watch the videos and casually join in on some of the discussions. Some people say that this limited engagement is a sign that MOOCs suffer from low retention rates. Far from it - we argue that any level of engagement is good engagement. On the other hand, if you are interested in taking this course at the University of Illinois, you can find more information about our program This course is one of a series of eight MOOCs created by Bill Cope and Mary Kalantzis for the Instructional Design and Leadership program at the University of Illinois. If you find this MOOC useful, please join us for more! e-Learning Ecologies: Innovative Approaches to Teaching and Learning in the Digital Age New Learning: Principles and Models of Pedagogy Assessment for Learning Learning, Knowledge, and Human Development Ubiquitous Learning and Instructional Technologies Negotiating Learner Differences: Towards Productive Diversity in Learning Literacy Teaching and Learning: Goals, Approaches, and Pedagogy Multimodal Literacies: Communication and Learning in the Age of Digital Media

AI-900: Microsoft Azure AI Fundamentals Practice Exam, a comprehensive and detailed assessment tool designed to help individuals prepare for the AI-900 certification exam. This practice exam is specifically tailored to cover all the essential topics and concepts that are required to successfully pass the Microsoft Azure AI Fundamentals exam. AI-900 certification exam is a fundamental level exam that validates an individual's knowledge and understanding of artificial intelligence concepts and how they are implemented in Microsoft Azure. By taking the AI-900: Microsoft Azure AI Fundamentals Practice Exam, candidates can assess their readiness for the actual exam and identify areas where they may need to focus their study efforts. AI-900 exam covers a wide range of topics, including machine learning, cognitive services, and natural language processing. By taking the time to study and prepare for this exam, you will gain a deep understanding of how AI technologies can be applied to real-world scenarios using Microsoft Azure. This knowledge will not only help you excel in your current role but also open up new opportunities for career advancement in the rapidly growing field of artificial intelligence. One of the key benefits of the AI-900: Microsoft Azure AI Fundamentals Practice Exam is that it is designed to closely mirror the format and content of the actual exam. This means that by completing this practice exam, you will be able to familiarize yourself with the types of questions you can expect to encounter on exam day. This will help you feel more confident and prepared, ultimately increasing your chances of passing the exam on your first attempt. In addition to helping you prepare for the exam, the AI-900: Microsoft Azure AI Fundamentals Practice Exam also provides valuable feedback on your performance. After completing the exam, you will receive a detailed score report that highlights your strengths and areas for improvement. This feedback can help you identify any gaps in your knowledge and tailor your study plan accordingly, ensuring that you are fully prepared to succeed on exam day. Another advantage of the AI-900: Microsoft Azure AI Fundamentals Practice Exam is that it is accessible anytime, anywhere. This means that you can study and practice at your own pace, fitting your exam preparation around your busy schedule. Whether you prefer to study in short bursts or dedicate long hours to exam preparation, this practice exam offers the flexibility you need to succeed. Furthermore, the AI-900: Microsoft Azure AI Fundamentals Practice Exam is an affordable and cost-effective way to prepare for the official exam. Rather than investing in expensive study materials or training courses, you can simply purchase this practice exam and start preparing right away. This makes it an ideal option for individuals looking to boost their knowledge and skills in AI without breaking the bank. AI-900 : Microsoft Azure AI Fundamentals Exam details : - Exam Name: Microsoft Certified - Azure AI Fundamentals - Exam Code: AI-900 - Exam Price: $99 (USD) - Number of Questions: Maximum of 40-60 questions, - Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based, - Length of Test: 60 Minutes. The exam is available in English and Japanese languages. - Passing Score: 700 / 1000 - Languages : English, Japanese, Korean, and Simplified Chinese - Schedule Exam : Pearson VUE AI-900 : Microsoft Azure AI Fundamentals Certification Exams skill questions: Skill Measurement Exam Topics: - Describe Artificial Intelligence workloads and considerations (15–20%) - Describe fundamental principles of machine learning on Azure (20–25%) - Describe features of computer vision workloads on Azure (15–20%) - Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%) - Describe features of generative AI workloads on Azure (15–20%) Describe Artificial Intelligence workloads and considerations (15–20%) Identify features of common AI workloads - Identify features of content moderation and personalization workloads - Identify computer vision workloads - Identify natural language processing workloads - Identify knowledge mining workloads - Identify document intelligence workloads - Identify features of generative AI workloads Identify guiding principles for responsible AI - Describe considerations for fairness in an AI solution - Describe considerations for reliability and safety in an AI solution - Describe considerations for privacy and security in an AI solution - Describe considerations for inclusiveness in an AI solution - Describe considerations for transparency in an AI solution - Describe considerations for accountability in an AI solution Describe fundamental principles of machine learning on Azure (20–25%) - Identify common machine learning techniques - Identify regression machine learning scenarios - Identify classification machine learning scenarios - Identify clustering machine learning scenarios - Identify features of deep learning techniques Describe core machine learning concepts - Identify features and labels in a dataset for machine learning - Describe how training and validation datasets are used in machine learning Describe Azure Machine Learning capabilities - Describe capabilities of automated machine learning - Describe data and compute services for data science and machine learning - Describe model management and deployment capabilities in Azure Machine Learning Describe features of computer vision workloads on Azure (15–20%) Identify common types of computer vision solution - Identify features of image classification solutions - Identify features of object detection solutions - Identify features of optical character recognition solutions - Identify features of facial detection and facial analysis solutions Identify Azure tools and services for computer vision tasks - Describe capabilities of the Azure AI Vision service - Describe capabilities of the Azure AI Face detection service Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%) Identify features of common NLP Workload Scenarios - Identify features and uses for key phrase extraction - Identify features and uses for entity recognition - Identify features and uses for sentiment analysis - Identify features and uses for language modeling - Identify features and uses for speech recognition and synthesis - Identify features and uses for translation Identify Azure tools and services for NLP workloads - Describe capabilities of the Azure AI Language service - Describe capabilities of the Azure AI Speech service Describe features of generative AI workloads on Azure (15–20%) Identify features of generative AI solutions - Identify features of generative AI models - Identify common scenarios for generative AI - Identify responsible AI considerations for generative AI Identify capabilities of Azure OpenAI Service - Describe natural language generation capabilities of Azure OpenAI Service - Describe code generation capabilities of Azure OpenAI Service - Describe image generation capabilities of Azure OpenAI Service In conclusion, the AI-900: Microsoft Azure AI Fundamentals Practice Exam is a valuable resource for anyone looking to enhance their understanding of AI technologies and their application in Microsoft Azure. By taking the time to study and prepare for this exam, you will not only increase your chances of passing the official exam but also gain a deeper appreciation for the power and potential of artificial intelligence. Don't wait any longer – invest in your future today with the AI-900: Microsoft Azure AI Fundamentals Practice Exam.

Welcome to our course on "Developing a One Page Website with OpenAI GPT API". In this course, you will learn how to build a one page website that integrates with the OpenAI GPT API, allowing you to display an output to website users based on some input they provide. The OpenAI GPT API is a powerful artificial intelligence tool that can help you build intelligent applications, and this course will teach you how to use this tool effectively. This course is suitable for beginners and those with some prior knowledge of web development. Throughout this course, you will learn how to use HTML, CSS, and JavaScript to design and develop a one page website that incorporates the OpenAI GPT API. You will learn how to use API keys to authenticate your requests and how to format your input data in JSON format for consumption by the API. By the end of this course, you will have developed a fully functional one page website that integrates with the OpenAI GPT API, and you will have the skills and knowledge to create powerful and innovative applications that leverage the power of artificial intelligence. Whether you are a web developer or a business owner, this course will help you take your skills to the next level and unlock the potential of the OpenAI ChatGPT API.

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