Understanding Azure AI and ML
Offline or Online Understanding Azure AI and ML training course.
This is the entry-level training, ideal for those starting their journey in AI and machine learning, and for people in non-technical roles who need a foundational understanding of AI concepts on Azure.
This course provides a comprehensive introduction to the AI and ML services offered by Microsoft AI.
Manoj S. Mahajan
28+ years Experienced Trainer with 100+ certs, View full profile....
Course Description
This course is designed to guide learners from the basics of Azure and AI theory to confidently using Azure's powerful AI services and understanding the machine learning workflow on the platform.
Audience:
- Developers, IT professionals, and data analysts new to AI/ML.
- Students and aspiring data scientists.
- Anyone interested in implementing AI solutions using the Microsoft Azure cloud.
Prerequisite:
- Basic understanding of cloud computing concepts.
- Familiarity with the Azure management console (creating VM, Storage account, Entra ID users, etc.).
- Foundational knowledge of a programming language (Python highly recommended).
- Recommended: Azure Fundamentals (AZ-900) or equivalent knowledge.
Syllabus
Please check the syllabus tab above. ☝What You'll Learn
Module 1: Introduction to Cloud Computing and Microsoft Azure ☁️
This module establishes the foundational knowledge of cloud computing and the Azure ecosystem, which is essential for working with Azure AI services.
- 1.1 Core Cloud Concepts
- Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS).
- Benefits: Scalability, high availability, and cost management.
- 1.2 Introduction to Microsoft Azure
- Tour of the Azure Portal and global infrastructure.
- Core Azure concepts: Subscriptions, Resource Groups, and Resources.
- Fundamental services: Azure Virtual Machines, Azure Storage (Blob), and Azure SQL Database.
- 1.3 The Azure AI Landscape
- Overview of the Azure AI platform.
- Distinguishing between pre-built Azure AI Services and the custom model platform, Azure Machine Learning.
Module 2: Foundational AI & Machine Learning Concepts
This module provides the theoretical underpinning necessary to understand how AI services work.
- 2.1 Demystifying AI, ML, and Deep Learning
- Key definitions and how they relate to each other.
- 2.2 Common Machine Learning Scenarios
- Supervised Learning: Classification (e.g., spam detection) and Regression (e.g., price prediction).
- Unsupervised Learning: Clustering (e.g., customer segmentation).
- 2.3 The Machine Learning Process
- From defining the problem to preparing data, training, evaluating, and deploying a model.
Module 3: Vision Services with Azure AI 👁️
Explore services that enable applications to see and interpret the world through images and videos.
- 3.1 Azure AI Vision
- Image analysis: Object detection, background removal, and generating captions.
- Optical Character Recognition (OCR) to extract printed and handwritten text.
- 3.2 Azure AI Face
- Face detection, verification, and identification.
- Understanding the responsible AI considerations for facial recognition.
- 3.3 Azure AI Custom Vision
- Training custom image classification and object detection models with your own images.
Module 4: Language Services with Azure AI 🗣️
Dive into services that process, understand, and generate human language.
- 4.1 Azure AI Language
- Sentiment Analysis: Determine positive, neutral, or negative sentiment in text.
- Key Phrase Extraction and Entity Recognition.
- Language Detection and Text Translation.
- 4.2 Azure AI Speech
- Speech-to-text: Transcribing audio into text.
- Text-to-speech: Creating natural-sounding synthetic voices.
- Speech Translation.
Module 5: Decision and Generative AI Services 🤖
Learn about services for intelligent decision-making, search, and content creation.
- 5.1 Azure AI Bot Service & Bot Framework
- Designing, building, and deploying intelligent chatbots.
- 5.2 Azure AI Search
- Implementing a powerful search experience over your content.
- 5.3 Introduction to Azure OpenAI Service
- Understanding large language models (LLMs) like GPT-4.
- Use cases: Content generation, summarization, and advanced conversational AI.
Module 6: Building Custom Models with Azure Machine Learning 🧠
This module provides an introduction to Azure's end-to-end platform for machine learning professionals.
- 6.1 Introduction to Azure Machine Learning
- Overview of the Azure ML Studio workspace.
- Key assets: Datasets, Compute, and Models.
- 6.2 Automated ML (AutoML)
- Automatically train, tune, and select the best model without writing extensive code.
- 6.3 Azure Machine Learning Designer
- Using a drag-and-drop interface to build and train models.
- 6.4 Model Deployment
- Deploying a trained model as a real-time web service (endpoint).
Module 7: Responsible AI and Course Wrap-up
This final module addresses the critical principles of creating fair, reliable, and ethical AI solutions.
- 7.1 Principles of Responsible AI
- Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, and Accountability.
- 7.2 Tools for Responsible AI in Azure
- Overview of the Responsible AI Dashboard in Azure Machine Learning for model debugging and fairness assessment.
- 7.3 Architecting Azure AI Solutions
- Best practices for combining multiple AI services to solve complex business problems.
Poonam
Overall a very informative training session. Course content got well covered and also demonstrated the concept very well. Read more....
Ganesh
It was good experience with trainer and teaching skill good and interact and answering every questions asked by student. Good knowledge and updated with his reliable courses. Read more....
Diptikesh
It was an excellent experience to have a training from Certification Guru. Thank you very much. Read more....
