Microsoft Azure AI Essentials: Workloads and Machine Learning on Azure

Course details

Instructor Ziggy Zulueta helps you develop a foundational understanding of the core concepts of AI, generative AI, natural language processing, computer vision, document intelligence, knowledge mining, content safety, and machine learning on Azure. Get a glimpse into the practical applications of each. Develop a familiarity with the specific Microsoft Azure AI and ML services available through demos. Learn valuable concepts on responsible AI and harnessing AI for business. After you complete this course and pass the accompanying exam you'll earn a professional certificate, providing an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services.

Learning objectives
Describe artificial intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure.
Describe features of natural language processing (NLP) workloads on Azure.
Describe features of document intelligence, knowledge mining, and content safety workloads on Azure.
Describe features of generative AI workloads on Azure.
Describe Microsoft’s six responsible AI principles.
Describe harnessing AI for business.

Concepts

0. Introduction

01 - The essentials of Azure AI

1. Understanding AI

02 - Definition, history, and evolution of AI
03 - Importance of learning AI

2. Azure Machine Learning

04 - Overview of machine learning
05 - Types of machine learning
06 - Understanding regression
07 - Binary classification
08 - Multiclass classification
09 - Understanding clustering
10 - Neural networks and deep learning
11 - Azure machine learning capabilities
12 - Practical application of machine learning in business
13 - Creating an Azure machine learning resource
14 - Azure machine learning demo

3. Computer Vision

15 - Overview of computer vision
16 - Deep learning for computer vision
17 - Introduction to Azure AI Vision
18 - Introduction to Azure AI Custom Vision
19 - Introduction to facial analysis and Azure AI Face
20 - Practical application of computer vision in business
21 - Creating an Azure AI Vision, Custom Vision, and Face resource
22 - Azure AI Vision demo
23 - Azure AI Custom Vision demo

4. Natural Language Processing

24 - Overview of natural language processing
25 - Introduction to Azure AI Language
26 - Introduction to Azure AI Translator
27 - Understanding speech recognition and synthesis
28 - Introduction to Azure AI Speech
29 - Practical application of natural language processing in business
30 - Creating an Azure AI Language and Azure AI Speech resource
31 - Azure AI Language demo
32 - Azure AI Speech demo

5. Document Intelligence, Content Safety, and Knowledge Mining

33 - Overview of document intelligence
34 - Azure AI Document Intelligence
35 - Introduction to Azure AI Content Safety
36 - Understanding knowledge mining and the elements of a search solution
37 - Introduction to Azure AI Search
38 - Practical application of these technologies in business
39 - Azure AI Document Intelligence demo
40 - Azure AI Content Safety demo
41 - Azure AI Search demo

6. Generative AI

42 - Overview of generative AI
43 - Traditional model development vs. foundation models
44 - Token, embeddings, transformer model
45 - Using language models - LLM vs. SLM
46 - Improving your prompts
47 - Introduction to Microsoft Copilot
48 - Customizing language models
49 - Introduction to Azure AI Studio
50 - Introduction to Azure OpenAI
51 - Practical application of generative AI in business
52 - Azure AI Studio demo

7. Responsible AI

53 - Importance of responsible use of AI
54 - Achieving AI fairness
55 - Achieving AI reliability and safety
56 - Achieving AI privacy and security
57 - Achieving AI inclusivity
58 - Achieving AI transparency
59 - Achieving AI accountability
60 - Real-life samples of responsible AI

8. Transform Your Business with Microsoft AI

61 - Microsoft AI approach
62 - Creating an AI strategy for your business
63 - AI opportunities in different industries
64 - Career opportunities in AI

Conclusion

65 - Key takeaways from the course
66 - Resources for further learning