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Exam Prep AI-900: Microsoft Certified Azure AI Fundamentals
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Whizlabs

Exam Prep AI-900: Microsoft Certified Azure AI Fundamentals

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5 modules
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

Recommended experience

Beginner level

Candidates need basic AI, ML, and Azure knowledge. No prior experience required, but cloud computing and client-server basics are beneficial.

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

5 modules
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

Recommended experience

Beginner level

Candidates need basic AI, ML, and Azure knowledge. No prior experience required, but cloud computing and client-server basics are beneficial.

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
  • About
  • Modules
  • Recommendations
  • Testimonials

Skills you'll gain

  • Microsoft Azure
  • Image Analysis
  • Artificial Intelligence
  • Deep Learning
  • Data Science
  • Computer Vision
  • Machine Learning
  • Natural Language Processing
  • Text Mining
  • Data Ethics
  • Generative AI
  • OpenAI

Details to know

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Recently updated!

May 2025

Assessments

14 assignments

Taught in English

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There are 5 modules in this course

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]

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.

What's included

20 videos3 readings4 assignments

20 videos•Total 106 minutes
  • Course Overview•5 minutes•Preview module
  • Exam Overview•6 minutes
  • Azure AI Services - Overview•5 minutes
  • What are Azure AI Solutions - Part 1•5 minutes
  • What are Azure AI Solutions - Part 2•6 minutes
  • Azure Machine Learning Services Overview•2 minutes
  • Data Science and Data Scientist - Overview•6 minutes
  • Data Scientist Skills - Overview•5 minutes
  • Data Science Processes - Overview•6 minutes
  • Machine Learning Solutions for Data Scientists•7 minutes
  • What is Machine Learning?•7 minutes
  • Types of Machine Learning•8 minutes
  • Exploring different data aspects in machine learning•8 minutes
  • Common AI Workloads - Overview•5 minutes
  • Responsible AI Guiding Principles•4 minutes
  • Reliability and safety in an AI solution•2 minutes
  • Privacy and security in an AI solution•3 minutes
  • Inclusiveness in an AI solution•3 minutes
  • Transparency in an AI solution•2 minutes
  • Accountability in an AI solution•1 minute
3 readings•Total 75 minutes
  • Welcome to the Course•35 minutes
  • Azure AI, ML and Data Science: Fundamentals - Overview•30 minutes
  • Meet and Greet•10 minutes
4 assignments•Total 145 minutes
  • Azure AI, ML and Data Science: Fundamentals - Graded Assignment•50 minutes
  • Course Introduction - Practice Assignment•30 minutes
  • Introduction to Azure ML and Data Science Concepts - Practice Assignment•35 minutes
  • Artificial Intelligence Workloads & Considerations - Practice Assignment•30 minutes

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.

What's included

10 videos1 reading2 assignments

10 videos•Total 64 minutes
  • Common terminologies used in Machine Learning•8 minutes•Preview module
  • Machine Learning Models•9 minutes
  • Deep Learning : Overview, Features and Techniques•3 minutes
  • Introduction to AutoML•7 minutes
  • Run an Automated Machine Learning experiment•7 minutes
  • Identify Data Source and Format•7 minutes
  • Identify Machine Learning Tasks•4 minutes
  • Choosing a Service to Train a ML Model•6 minutes
  • Features and labels in a dataset for machine learning•4 minutes
  • Training and validation datasets in machine learning•4 minutes
1 reading•Total 30 minutes
  • Azure Machine Learning Principles - Overview•30 minutes
2 assignments•Total 90 minutes
  • Azure Machine Learning Principles - Graded Assignment•50 minutes
  • Azure Machine Learning: Core Concepts, Techniques and Capabilities - Practice Assignment•40 minutes

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.

What's included

11 videos1 reading2 assignments

11 videos•Total 62 minutes
  • Image classification•5 minutes•Preview module
  • Object detection•4 minutes
  • Optical character recognition•2 minutes
  • Object Detection and Image Tagging in Azure AI Vision•9 minutes
  • OCR for images - Azure AI Vision•6 minutes
  • Azure AI Vision - Overview•5 minutes
  • Azure AI Custom Vision - Overview•6 minutes
  • Azure AI Custom Vision - Demo•5 minutes
  • Azure AI Face service: Overview•3 minutes
  • Azure AI Face service: Demo - Part1•6 minutes
  • Azure AI Face service: Demo - Part2•5 minutes
1 reading•Total 30 minutes
  • Azure Computer Vision: Solutions and Tools - Overview•30 minutes
2 assignments•Total 65 minutes
  • Azure Computer Vision: Solutions, Features, and Tools - Graded Assignment•35 minutes
  • Azure Computer Vision: Solutions and Tools - Practice Assignment•30 minutes

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.

What's included

13 videos1 reading3 assignments

13 videos•Total 61 minutes
  • Natural language processing [NLP]: Overview, Workload Scenarios and Features•5 minutes•Preview module
  • Key phrase extraction•3 minutes
  • Entity recognition•2 minutes
  • Sentiment analysis•4 minutes
  • Language detection•3 minutes
  • Speech recognition and synthesis•4 minutes
  • Uses for translation•3 minutes
  • Azure AI Speech Service: Overview•5 minutes
  • Azure AI Speech Service: Demo - Part1•6 minutes
  • Azure AI Speech Service: Demo - Part2•4 minutes
  • Azure AI Language - Part 1•6 minutes
  • Azure AI Language - Part 2•5 minutes
  • Azure AI Language - Demo•6 minutes
1 reading•Total 30 minutes
  • Azure Natural Language Processing (NLP): Scenarios, Features, and Tools - Overview•30 minutes
3 assignments•Total 100 minutes
  • Azure Natural Language Processing (NLP): Scenarios, Features, and Tools - Graded Assignment•40 minutes
  • NLP Workload Overview & Features - Practice Assignment•30 minutes
  • Azure tools and services for NLP workloads - Practice Assignment•30 minutes

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.

What's included

13 videos3 readings3 assignments

13 videos•Total 82 minutes
  • Generative AI Solutions - Overview•8 minutes•Preview module
  • Generative AI Features and Common Scenarios•6 minutes
  • Responsible AI considerations for generative AI•5 minutes
  • Azure OpenAI - Overview and Service Models Part 1•7 minutes
  • Azure OpenAI - Overview and Service Models Part 2•8 minutes
  • Generate images with Azure OpenAI Service•6 minutes
  • Azure OpenAI Service - Natural Language Solutions•9 minutes
  • Azure AI Foundry - Overview and Demo•5 minutes
  • Retrieval Augmented Generation (RAG) in Azure AI and ML: Overview•4 minutes
  • Optimizing Models: Fine-Tuning, RAG and Application Strategies•6 minutes
  • Model Catalog and Collections [Azure AI Foundry and ML]-Overview•4 minutes
  • Model Catalog and Collections [Azure AI Foundry and ML]-Compute•3 minutes
  • Summary and What's Next and Best Practices•5 minutes
3 readings•Total 60 minutes
  • Generative AI workloads on Azure - Overview•30 minutes
  • What's Next?•10 minutes
  • Course Conclusion and Key Takeaways•20 minutes
3 assignments•Total 110 minutes
  • Generative AI workloads on Azure - Graded Assignment•50 minutes
  • Azure OpenAI: Service Models & Capabilities - Practice Assignment•30 minutes
  • Azure AI Foundry: Service Models & Capabilities - Practice Assignment•30 minutes

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