Exam Prep AI-900: Microsoft Certified Azure AI Fundamentals

Whizlabs via Coursera

Go to class Write review

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

This course provides a comprehensive understanding of Azure AI, Machine Learning, and Data Science, integrating fundamental concepts with advanced tools and solutions. You will explore core principles of Azure Machine Learning, delve into powerful Computer Vision and Natural Language Processing (NLP) features, and unlock generative AI capabilities with Azure OpenAI and Azure AI Foundry. The course emphasizes practical knowledge, guiding you through real-world applications to build intelligent solutions. The AI-900 course introduces the fundamental concepts of AI and the services available in Microsoft Azure to create AI solutions. It focuses on building awareness of common AI workloads and identifying Azure services to support them. The course includes: This course facilitates learners with approximately 6:30-7:00 Hours of Video lectures that provide both Theory and Hands-On knowledge. The course is divided into 5 Modules, each further divided into lessons. To test learners' understanding, every module includes Assignments in the form of Quizzes and In-Video Questions. Module 1: Azure AI, ML, and Data Science: Fundamentals Module 2: Azure Machine Learning Principles Module 3: Azure Computer Vision: Solutions and Tools Module 4: Azure Natural Language Processing (NLP): Scenarios, Features, and Tools Module 5: Generative AI workloads on Azure [Azure OpenAI - Azure AI Foundry]

Syllabus

Azure AI, ML and Data Science: Fundamentals

This week provides a comprehensive introduction to Azure AI and Machine Learning services, focusing on their core capabilities, components, and real-world applications. Learners will gain insight into the tools and technologies that drive intelligent solutions on Azure and explore the role of a data scientist in the AI development lifecycle. This week also covers key machine learning concepts, the various types of AI workloads, and how to evaluate the effectiveness of AI solutions. Additionally, learners will become familiar with Microsoft’s Responsible AI principles and best practices, equipping them to design and implement ethical, secure, and inclusive AI systems.

Azure Machine Learning Principles

This week provides a foundational understanding of machine learning concepts and terminology, focusing on key elements such as common ML models, the roles of features and labels, and the distinctions between training and validation datasets. Learners will also be introduced to deep learning techniques and gain hands-on experience with Automated Machine Learning (AutoML) experiments. By the end of this week, learners will be equipped with the knowledge to identify machine learning tasks, select the appropriate Azure services, and begin developing and training their own ML models with confidence and efficiency.

Azure Computer Vision: Solutions, Features, and Tools

This week provides a comprehensive understanding of Azure AI Vision and its key capabilities, including image classification, object detection, and optical character recognition (OCR). Learners will explore how these services are applied in real-world scenarios and gain hands-on experience with Azure AI Custom Vision to build and deploy models for specific image tagging and detection tasks. Additionally, the module covers the Azure AI Face service, focusing on facial detection and recognition through practical demonstrations. By the end of this week, learners will be equipped with the knowledge and skills to design and implement intelligent vision solutions using Azure’s powerful AI tools.

Azure Natural Language Processing (NLP): Scenarios, Features, and Tools

This week provides a comprehensive understanding of Natural Language Processing (NLP) and speech technologies using Azure AI services. Learners will explore essential NLP capabilities, such as key phrase extraction, sentiment analysis, language detection, and entity recognition. The module also covers the use of Azure AI Speech for voice recognition and synthesis, enabling the creation of voice-enabled applications. Additionally, learners will delve into Azure’s translation services to implement multilingual solutions that facilitate global communication. By the end of this week , learners will have the skills to design and implement advanced language solutions using Azure AI, including text analysis and custom language model development.

Generative AI workloads and Features on Azure

This module provides a comprehensive overview of Generative AI, focusing on its foundational concepts, key features, and real-world applications. Learners will gain insights into responsible AI practices when deploying generative models, ensuring ethical and safe AI development. The module also explores the powerful capabilities of Azure OpenAI services, including code generation, image creation, and natural language processing. Additionally, learners will dive into Azure AI Foundry to explore advanced tools like Retrieval Augmented Generation (RAG) and model optimization strategies, empowering them to enhance AI and ML workflows. By the end of this module, learners will have the practical knowledge required to fine-tune models, optimize performance, and deploy robust AI solutions effectively.