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via Udemy |
Go to Course: https://www.udemy.com/course/ai-900-microsoft-azure-ai-fundamentals-95-practice-test/
Certainly! Here's a comprehensive review, detailed overview, and recommendation for the Microsoft Certified: Azure AI Fundamentals AI-900 course on Coursera: --- ### Course Review: Microsoft Certified: Azure AI Fundamentals AI-900 Are you interested in gaining foundational knowledge of artificial intelligence and machine learning on Azure? The **Azure AI Fundamentals (AI-900)** course on Coursera is an excellent starting point for both technical and non-technical professionals aiming to understand how AI workloads are built and implemented using Microsoft Azure. With over **3,800 students enrolled**, this course has proven to be popular and effective. Its practical approach revolves around practicing with a set of updated, pattern-based practice tests designed to boost your confidence and readiness for the AI-900 exam. These practice tests are regularly refreshed to include new questions, ensuring you stay up-to-date with the latest exam formats and patterns. ### Course Content & Learning Objectives: This course covers essential AI concepts such as: - **AI workloads and considerations**: Understanding different AI applications and their implementation requirements. - **Fundamental principles of machine learning on Azure**: Learning how machine learning models are created, managed, and deployed. - **Computer Vision workloads**: Gaining insights into how visual data is interpreted and processed. - **Natural Language Processing (NLP)**: Exploring how machines interpret and generate human language. - **Conversational AI**: Building intelligent chatbots and virtual agents for engaging in human-like dialogues. ### Why This Course is Valuable: - **No prerequisites needed**: The course is designed for individuals with varied backgrounds, making it accessible to non-technical professionals, though some programming experience can be advantageous. - **Preparation for multiple certifications**: While this course prepares you specifically for the AI-900 exam, it also lays a solid foundation for higher-level certifications like Azure Data Scientist or AI Engineer. - **Practical approach**: The emphasis on practice tests ensures you can confidently approach the actual exam. ### Course Highlights: - Clear explanations of AI workloads, including anomaly detection, computer vision, NLP, and conversational AI. - An overview of Microsoft Azure's machine learning services, such as Azure Machine Learning Service, Automated Machine Learning, and the ML Designer. - Focus on real-world applications and practical skills. ### Recommendation: If you're looking for a beginner-friendly, comprehensive resource to kickstart your AI journey on Azure, this Coursera course is highly recommended. The combination of detailed content, real-world examples, and rigorous practice tests makes it a top choice for exam preparation. Moreover, the course’s regular updates with new questions reflect the latest exam patterns, giving you an edge in the certification process. Enrolling now will give you the confidence and knowledge necessary to succeed in the AI-900 exam and to further pursue advanced Azure AI certifications. --- **In summary:** The Microsoft Azure AI Fundamentals (AI-900) course on Coursera is an outstanding, trusted resource to learn AI basics on Azure, with practical tests and up-to-date materials to ensure your success. Whether you're new to the field or looking to validate your skills with a certification, this course provides everything you need to get started. --- Would you like a personalized study plan or tips for exam day as well?
You must practice these practice tests to get confident in AI-900 Exam and give your best in the actual exam to get certified successfully.This set of practice tests will be get updated, so you can get more questions to great learning. It's based on a new question pattern.[3800++ Students have been enrolled for this course.]Microsoft Certified: Azure AI Fundamentals AI-900This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it's not a prerequisite for any of them.Skills measured AI 900Describe AI workloads and considerationsDescribe fundamental principles of machine learning on AzureDescribe features of computer vision workloads on AzureDescribe features of Natural Language Processing (NLP) workloads on AzureDescribe features of conversational AI workloads on Azure---------------------------------------------------------------------------------Overview of AIAI is the creation of software that imitates human behaviors and capabilities. Key elements include:Machine learning - This is often the foundation for an AI system, and is the way we "teach" a computer model to make predictions and draw conclusions from data.Anomaly detection - The capability to automatically detect errors or unusual activity in a system.Computer vision - The capability of software to interpret the world visually through cameras, video, and images.Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind.Conversational AI - The capability of a software "agent" to participate in a conversation.Azure Machine LearningMachine Learning is the basis of most AI solutions.Microsoft Azure offers the following: Azure Machine Learning Service - A cloud-based platform that allows you to create, manage and publish machine learning models. Azure Machine Learning offers the following capabilities and features:Automated machine-learning: This feature allows non-experts to create machine learning models quickly from data.Azure Machine Learning designer: An interface that allows for no-code creation of machine learning solutions.Data and compute management: Professional data scientists can access cloud-based data storage and compute resources to run code for data experiments at scale.Pipelines: Software engineers, data scientists, and IT operations professionals are able to create pipelines that can be used to manage model deployment, training, and management.