Curious about artificial intelligence? Want to understand what the buzz is about? This module introduces you to the world of AI.
In this module, you learn about the kinds of solutions AI can make possible and considerations for responsible AI practices.
Ever wondered how AI can create content, answer questions, and assist with tasks? This module introduces you to the world of generative AI and agents.
By the end of this module, you'll be able to:
- Describe core concepts of generative AI.
- Explain how large language models (LLMs) work.
- Consider how to create effective prompts for LLMs.
- Describe core concepts of agents and agentic AI solutions.
Natural language processing (NLP) supports applications that can analyze text to infer semantic meaning.
Explore concepts and techniques for text analysis.
Imagine AI apps and agents that you can talk to. Explore the concepts behind AI speech, including speech recognition and synthesis.
After completing this module, you'll be able to:
- Identify different scenarios for AI speech
- Describe how speech recognition works
- Describe how speech synthesis works
Introduction to computer vision concepts
After completing this module, you will be able to:
- Identify different types of computer vision tasks
- Describe how filters are used in image analysis
- Describe the main features of a convolutional neural network (CNN)
- Describe the main features of a vision transformer (ViT)
- Describe how generative AI can be used to create images
Introduction to AI-powered information extraction concepts
After completing this module, you will be able to:
- Understand the key concepts of information extraction.
- Describe how optical character recognition (OCR) extracts text from images.
- Explain how form extraction maps extracted text to data fields.
Learn how retrieval-augmented generation (RAG) grounds generative AI responses in relevant information.
By the end of this module, you'll be able to:
- Explain the purpose and benefits of retrieval-augmented generation (RAG).
- Describe how data is prepared for retrieval.
- Describe how a RAG solution retrieves relevant information and uses it to generate a grounded response.
- Identify considerations for evaluating and improving a RAG solution.

Syllabus

Introduction to AI concepts
- Introduction to AI
- Generative AI and agents
- Text and natural language
- Speech
- Computer vision
- Information extraction
- Responsible AI
- Exercise - Explore AI workloads
- Module assessment
- Summary
Introduction to generative AI and agents
- Introduction
- Large language models (LLMs)
- Prompts
- AI agents
- Exercise - Explore generative AI
- Module assessment
- Summary
Introduction to natural language processing concepts
- Introduction
- Tokenization
- Statistical text analysis.
- Semantic language models
- Exercise - Explore text analytics
- Module assessment
- Summary
Introduction to AI speech concepts
- Introduction
- Speech-enabled solutions
- Speech recognition
- Speech synthesis
- Exercise - Explore AI speech
- Module assessment
- Summary
Introduction to computer vision concepts
- Introduction
- Computer vision tasks and techniques
- Images and image processing
- Convolutional neural networks
- Vision transformers and multimodal models
- Image generation
- Exercise - Explore computer vision
- Module assessment
- Summary
Introduction to AI-powered information extraction concepts
- Introduction
- Overview of information extraction
- Optical character recognition (OCR)
- Field extraction and mapping
- Exercise - Explore AI information extraction
- Module assessment
- Summary
Introduction to retrieval-augmented generation concepts
- Introduction
- Understand retrieval-augmented generation
- Prepare data for retrieval
- Retrieve information and generate a response
- Evaluate a RAG solution
- Exercise - Explore RAG
- Module assessment
- Summary