Azure AI Services: Core applications

Overview

"Core Applications of Azure AI Services" is an introductory course designed to familiarize users with the practical application of Microsoft's Azure AI services in various real-world scenarios. It aims to bridge the gap between theoretical AI concepts and their actual implementation.

You will learn how Azure AI services are utilized to solve real-world problems. The content focuses on the practical side of AI deployment, showcasing how different Azure AI tools can be leveraged in various industries and applications.

You will gain insights into the reasoning behind using specific AI services and understand the general steps involved in implementing these solutions. The course emphasizes the "why" and "how" of using Azure AI, providing a foundational understanding of its capabilities and potential.

It's geared towards those new to applying AI, offering a starting point for exploring and understanding Azure AI services in action.

Syllabus

• Introduction to Azure AI Services
Overview of Microsoft's AI offerings
Understanding AI vs. Machine Learning vs. Cognitive Services
The role of AI in modern applications
• Setting up Your Azure Environment
Creating an Azure account
Navigating the Azure Portal
Setting up a development environment
• Core Azure AI Services
Azure Cognitive Services
Vision: Image processing and computer vision applications
Speech: Speech recognition and synthesis
Language: Natural language processing and text analytics
Decision: Anomaly detection and content moderation
Azure Machine Learning
Creating and managing machine learning models
Deploying machine learning experiments
Azure Bot Services
Building and deploying chatbots
Integrating bots with other Azure services
• Practical Applications and Industry Use Cases
AI in Healthcare: Patient data analysis and diagnostics
AI in Finance: Fraud detection and personalized banking
AI in Retail: Customer recommendations and inventory management
AI in Manufacturing: Predictive maintenance and process optimization
• Implementation Steps for Azure AI Solutions
Defining business problems and objectives
Selecting appropriate AI services
Data preparation and management
Model training and evaluation
Deployment and monitoring
• Hands-on Labs and Projects
Building a simple computer vision application
Developing a chatbot for customer service
Implementing a sentiment analysis tool for social media monitoring
• Best Practices and Considerations
Ethical AI and governance
Ensuring data security and privacy
Scalability and performance optimization
• Conclusion and Next Steps
Recap of key learnings
Exploring advanced topics in Azure AI
Further resources and learning paths

Subjects

Programming