15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for:
1. IT professionals and business analysts
2. Developers and software engineers
3. Data scientists and data analysts
4. Solutions architects
5. Students and career changers
6. Project managers
7. Enterprise decision-makers
8. Cloud engineers
9. Technical managers
10. Data engineers
Session Objectives
- Understand core artificial intelligence concepts and their real-world applications.
- Describe the different types of AI workloads on Azure.
- Identify the key services within the Azure AI platform.
- Implement Azure Cognitive Services for common use cases.
- Build and train basic machine learning models using Azure Machine Learning Studio.
- Understand the principles of responsible AI and bias detection.
- Learn about vision, speech, and natural language processing capabilities.
- Explore conversation AI services and bot creation.
- Differentiate between classic machine learning and deep learning.
- Gain foundational knowledge required for the AI-900 certification exam.
About the Course
The landscape of artificial intelligence is rapidly evolving, making it essential for IT professionals and developers to possess a foundational understanding of AI principles and their practical applications. This training course introduces participants to the core concepts of artificial intelligence and the powerful, ready-to-use AI services offered within the Microsoft Azure ecosystem. From pre-built cognitive services that can be integrated into applications with minimal code to foundational machine learning tools, participants will learn how to leverage Azure to build intelligent solutions and unlock new business value. This program focuses on providing a comprehensive overview of the Microsoft Azure AI platform, empowering attendees with the knowledge to identify suitable AI services for different business challenges. The curriculum is designed to be accessible to a wide audience, offering a mix of theoretical knowledge and practical, hands-on labs. Participants will explore core AI capabilities such as machine learning, computer vision, and natural language processing, all while gaining an understanding of the critical importance of responsible AI development and deployment.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Defining artificial intelligence and its sub-fields
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Subtopic 1.2:
Understanding the lifecycle of an AI project
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Subtopic 1.3:
Distinguishing between AI, machine learning, and deep learning
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Subtopic 1.4:
Exploring real-world applications of AI
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Subtopic 1.5:
The role of AI in digital transformation
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Subtopic 2.1:
An overview of Azure Machine Learning
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Subtopic 2.2:
The different types of machine learning (supervised, unsupervised, reinforcement)
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Subtopic 2.3:
Key machine learning concepts and terminology
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Subtopic 2.4:
Introduction to the Azure Machine Learning Studio
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Subtopic 2.5:
Understanding the basics of model training and evaluation
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Subtopic 3.1:
Building and training regression models
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Subtopic 3.2:
Understanding the concept of classification
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Subtopic 3.3:
Evaluating model performance for classification tasks
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Subtopic 3.4:
Hands-on lab for a basic classification model
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Subtopic 3.5:
Interpreting the results of a simple ML model
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Subtopic 4.1:
Introduction to the Azure AI Vision service
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Subtopic 4.2:
The difference between object detection and image classification
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Subtopic 4.3:
Using the Vision API to analyze images
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Subtopic 4.4:
Applying facial recognition and analysis
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Subtopic 4.5:
Key use cases for computer vision in business
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Subtopic 5.1:
An introduction to natural language processing (NLP)
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Subtopic 5.2:
Key features of the Azure AI Language service
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Subtopic 5.3:
Extracting key phrases and entities from text
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Subtopic 5.4:
Using sentiment analysis to understand customer feedback
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Subtopic 5.5:
Building a simple text classification model
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Subtopic 6.1:
The role of chatbots and virtual assistants
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Subtopic 6.2:
An overview of Azure AI Bot Service
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Subtopic 6.3:
Designing a conversational flow
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Subtopic 6.4:
Understanding question answering and knowledge bases
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Subtopic 6.5:
Integrating a bot with various channels
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Subtopic 7.1:
The components of the Azure AI Speech service
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Subtopic 7.2:
The components of the Azure AI Speech service
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Subtopic 7.3:
The concept of speaker recognition and identification
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Subtopic 7.4:
Using custom speech models for unique vocabularies
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Subtopic 7.5:
Real-world applications of speech AI
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Subtopic 8.1:
Anomaly detection in time series data
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Subtopic 8.2:
Content safety and moderation services
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Subtopic 8.3:
Document intelligence for data extraction
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Subtopic 8.4:
Personalizer for creating personalized experiences
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Subtopic 8.5:
Using Azure AI Search to build intelligent search applications
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Subtopic 9.1:
The importance of ethical AI development
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Subtopic 9.2:
Understanding the six key principles of responsible AI
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Subtopic 9.3:
Bias and fairness in machine learning models
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Subtopic 9.4:
Tools for ensuring transparency and accountability
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Subtopic 9.5:
Building AI systems that are safe and reliable
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Subtopic 10.1:
Overview of the Azure AI platform
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Subtopic 10.2:
Integrating different AI services within a single solution
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Subtopic 10.3:
Managing AI resources in the Azure portal
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Subtopic 10.4:
Cost management and monitoring for AI services
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Subtopic 10.5:
Understanding the roles and responsibilities in an AI project
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Subtopic 11.1:
Introduction to the Azure Machine Learning designer
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Subtopic 11.2:
Drag-and-drop model building
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Subtopic 11.3:
Pre-built modules and datasets
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Subtopic 11.4:
Publishing and deploying models without writing code
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Subtopic 11.5:
Visualizing and understanding data flow
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Subtopic 12.1:
The concept of MLOps and its importance
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Subtopic 12.2:
Automating the machine learning lifecycle
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Subtopic 12.3:
Versioning models and datasets
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Subtopic 12.4:
Monitoring model performance in production
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Subtopic 12.5:
Implementing continuous integration and continuous deployment (CI/CD)
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Subtopic 13.1:
The role of data in machine learning
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Subtopic 13.2:
Cleaning and preparing data for training
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Subtopic 13.3:
Feature engineering and selection
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Subtopic 13.4:
Handling missing values and outliers
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Subtopic 13.5:
Data security and privacy in AI workflows
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Subtopic 14.1:
Using Azure AI Custom Vision to build custom models
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Subtopic 14.2:
Training a model to identify specific objects
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Subtopic 14.3:
The process of tagging images and training
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Subtopic 14.4:
Evaluating and improving a custom vision model
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Subtopic 14.5:
Exporting and deploying a custom vision model
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Subtopic 15.1:
The basics of large language models (LLMs)
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Subtopic 15.2:
Using the Azure OpenAI Service
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Subtopic 15.3:
Understanding prompts and prompt engineering
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Subtopic 15.4:
Building simple applications with generative AI
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Subtopic 15.5:
The future of generative AI and its impact
