Introduction

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.

Duration:

10 days

Target Audience:

• IT professionals and business analysts
• Developers and software engineers
• Data scientists and data analysts
• Solutions architects
• Students and career changers
• Project managers
• Enterprise decision-makers
• Cloud engineers
• Technical managers
• Data engineers

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.

Module 1: Introduction to AI

• Defining artificial intelligence and its sub-fields
• Understanding the lifecycle of an AI project
• Distinguishing between AI, machine learning, and deep learning
• Exploring real-world applications of AI
• The role of AI in digital transformation

Module 2: Machine Learning on Azure

• An overview of Azure Machine Learning
• The different types of machine learning (supervised, unsupervised, reinforcement)
• Key machine learning concepts and terminology
• Introduction to the Azure Machine Learning Studio
• Understanding the basics of model training and evaluation

Module 3: Regression and Classification

• Building and training regression models
• Understanding the concept of classification
• Evaluating model performance for classification tasks
• Hands-on lab for a basic classification model
• Interpreting the results of a simple ML model

Module 4: Computer Vision Fundamentals

• Introduction to the Azure AI Vision service
• The difference between object detection and image classification
• Using the Vision API to analyze images
• Applying facial recognition and analysis
• Key use cases for computer vision in business

Module 5: Natural Language Processing

• An introduction to natural language processing (NLP)
• Key features of the Azure AI Language service
• Extracting key phrases and entities from text
• Using sentiment analysis to understand customer feedback
• Building a simple text classification model

Module 6: Conversational AI

• The role of chatbots and virtual assistants
• An overview of Azure AI Bot Service
• Designing a conversational flow
• Understanding question answering and knowledge bases
• Integrating a bot with various channels

Module 7: Speech AI

• The components of the Azure AI Speech service
• Converting speech to text and text to speech
• The concept of speaker recognition and identification
• Using custom speech models for unique vocabularies
• Real-world applications of speech AI

Module 8: Other Cognitive Services

• Anomaly detection in time series data
• Content safety and moderation services
• Document intelligence for data extraction
• Personalizer for creating personalized experiences
• Using Azure AI Search to build intelligent search applications

Module 9: Responsible AI Principles

• The importance of ethical AI development
• Understanding the six key principles of responsible AI
• Bias and fairness in machine learning models
• Tools for ensuring transparency and accountability
• Building AI systems that are safe and reliable

Module 10: Azure AI Platform Components

• Overview of the Azure AI platform
• Integrating different AI services within a single solution
• Managing AI resources in the Azure portal
• Cost management and monitoring for AI services
• Understanding the roles and responsibilities in an AI project

Module 11: No-Code AI with Designer

• Introduction to the Azure Machine Learning designer
• Drag-and-drop model building
• Pre-built modules and datasets
• Publishing and deploying models without writing code
• Visualizing and understanding data flow

Module 12: Machine Learning Operations (MLOps)

• The concept of MLOps and its importance
• Automating the machine learning lifecycle
• Versioning models and datasets
• Monitoring model performance in production
• Implementing continuous integration and continuous deployment (CI/CD)

Module 13: Data and Feature Engineering

• The role of data in machine learning
• Cleaning and preparing data for training
• Feature engineering and selection
• Handling missing values and outliers
• Data security and privacy in AI workflows

Module 14: Computer Vision with Custom Vision

• Using Azure AI Custom Vision to build custom models
• Training a model to identify specific objects
• The process of tagging images and training
• Evaluating and improving a custom vision model
• Exporting and deploying a custom vision model

Module 15: Introduction to Generative AI

• The basics of large language models (LLMs)
• Using the Azure OpenAI Service
• Understanding prompts and prompt engineering
• Building simple applications with generative AI
• The future of generative AI and its impact

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.