AI Simplified for Everyone

Understand what Artificial Intelligence is, how it works at a high level, and where it is used in everyday life.

What you will learn:

• What Artificial Intelligence (AI) is and how it simulates human intelligence

• Key characteristics and capabilities of AI systems

• Examples of AI in real-world applications

After completing this, you will be able to:

• Explain the basic concept of AI in simple terms

• Identify where AI is used in daily life and business

• Build a strong foundation for learning advanced AI topics

Learn how Machine Learning enables systems to learn from data and improve over time without explicit programming.

What you will learn:

• What Machine Learning is and how it differs from traditional programming

• Types of Machine Learning (supervised, unsupervised, etc.)

• Common use cases of Machine Learning

After completing this, you will be able to:

• Understand how machines learn from data

• Differentiate between types of Machine Learning

• Identify practical applications of Machine Learning

Explore Deep Learning as an advanced form of Machine Learning and understand its role in handling complex data.

What you will learn:

• What Deep Learning is and how it builds on Machine Learning

• Basics of neural networks and how they work

• Real-world applications of Deep Learning

After completing this, you will be able to:

• Explain Deep Learning in simple terms

• Understand the role of neural networks

• Recognize where Deep Learning is applied

Understand how Generative AI creates new content and how it differs from traditional AI approaches.

What you will learn:

• What Generative AI is and how it works

• Differences between Generative AI and traditional (discriminative) AI

• Examples of Generative AI applications

After completing this, you will be able to:

• Explain how Generative AI creates content

• Differentiate between Generative and traditional AI

• Identify use cases of Generative AI

Get a high-level view of the complete AI lifecycle and understand the key stages involved in building AI systems.

What you will learn:

• What the AI lifecycle is and why it is important

• Key stages involved in building an AI system

• How different steps connect to form a complete workflow

After completing this, you will be able to:

• Describe the overall AI lifecycle

• Identify key stages in AI development

• Understand how AI systems move from data to deployment

Explore each stage of the AI lifecycle in detail and understand how AI systems are developed, trained, and deployed.

What you will learn:

• Key stages such as data collection, preparation, model training, and deployment

• The role of data in building effective AI systems

• How AI models are improved and maintained over time

After completing this, you will be able to:

• Explain each stage of the AI lifecycle in simple terms

• Understand how AI systems are built step-by-step

• Recognize the importance of data and continuous improvement

Understand the real-world challenges in handling customer interactions and how Conversational AI helps solve them.

What you will learn:

• Challenges in managing large volumes of customer conversations

• Why understanding user intent is important

• How Conversational AI helps automate interactions

After completing this, you will be able to:

• Identify problems solved by Conversational AI

• Explain how AI improves customer interaction processes

• Understand the need for chatbot-based solutions

Learn what Amazon Lex is and how it enables the creation of conversational AI solutions like chatbots and voice assistants.

What you will learn:

• What Amazon Lex is and its key capabilities

• How it uses AI to understand and respond to user input

• Core concepts like intent, data collection, and action

After completing this, you will be able to:

• Explain how Amazon Lex works at a high level

• Understand the components of a chatbot

• Connect Conversational AI concepts to a real tool

Watch a practical demonstration of how a chatbot is created and configured using Amazon Lex.

What you will learn:

• Steps involved in creating a chatbot using Amazon Lex

• How intents, data inputs, and configurations are set up

• How conversational AI solutions are implemented in practice

After completing this, you will be able to:

• Understand the process of building a chatbot

• Relate concepts to real-world implementation

• Gain confidence in using AI tools for practical use cases

A quick recap of the chatbot demo, summarizing the key steps involved in creating and configuring a conversational AI solution using Amazon Lex.

Understand what a prompt is and how it guides AI systems to generate relevant and accurate responses.

What you will learn:

• What a prompt is and its role in AI interactions

• How prompts influence AI outputs

• Basic structure of an effective prompt

After completing this, you will be able to:

• Define what a prompt is

• Understand how prompts guide AI behavior

• Identify key elements of a good prompt

Learn how to design structured prompts to improve clarity, control, and consistency in AI responses.

What you will learn:

• What prompt engineering is and why it is important

• Key components like instructions, context, and examples

• How to structure prompts for better results

After completing this, you will be able to:

• Create clear and effective prompts

• Improve the quality of AI-generated outputs

• Apply prompt design techniques in real scenarios

Explore different prompting techniques used to handle simple and complex tasks with AI systems.

What you will learn:

• Techniques such as zero-shot and few-shot prompting

• How to guide AI for reasoning and step-by-step tasks

• Using prompts for text and image generation

After completing this, you will be able to:

• Choose the right prompting technique for a task

• Improve results for complex queries

• Apply prompting across different use cases

Understand what Generative AI is, how it creates content, and how it differs from traditional AI approaches.

What you will learn:

• What Generative AI is and how it works

• How AI generates outputs from inputs

• Differences between Generative and traditional (discriminative) AI

After completing this, you will be able to:

• Explain Generative AI in simple terms

• Understand how AI generates content

• Differentiate between Generative and traditional AI

  
  

Learn about foundational models, how they are trained on large datasets, and the core building blocks of AI, including neural networks.

What you will learn:

• What foundational models are

• How they are trained on large datasets

• Basics of neural networks as the building blocks of AI

After completing this, you will be able to:

• Understand the role of foundational models

• Explain how neural networks enable AI capabilities

• Identify examples of commonly used models

Explore popular Generative AI tools and understand how they are used in real-world applications.

What you will learn:

• Overview of tools like ChatGPT, Gemini, Claude, and Amazon Q

• How these tools are used for different tasks

• Practical use cases of Generative AI tools

After completing this, you will be able to:

• Identify commonly used AI tools

• Understand how to apply these tools in daily tasks

• Choose the right tool for different use cases

Understand what AI Agents are, how they move from generating responses to taking actions, explore their key capabilities and use cases, and learn how they work through the agent loop.

What you will learn:

• What AI Agents are and how they move from generating to taking actions

• Key capabilities and real-world use cases of AI Agents

• How AI Agents work using the agent loop

After completing this, you will be able to:

• Explain AI Agents and their capabilities

• Identify practical use cases of AI Agents

• Understand how AI Agents execute tasks step-by-step

Understand what Model Context Protocol (MCP) is, how it enables AI to connect with tools and data, and explore how agentic AI architecture brings together different components to execute tasks.

What you will learn:

• What MCP is and how it connects AI with systems and data

• How MCP works to enable structured and secure interactions

• How agentic AI architecture enables AI systems to plan and execute tasks

After completing this, you will be able to:

• Explain MCP and its role in AI systems

• Understand how AI interacts with external tools and data

• Visualize how AI components work together to complete tasks

Understand the basic principles and concerns that guide responsible use of AI systems.

What you will learn:

• Why ethical considerations are important in AI

• Common risks such as bias and misuse of data

• Basic guidelines for responsible AI usage

After completing this, you will be able to:

• Recognize ethical concerns in AI systems

• Understand risks associated with AI usage

• Apply basic principles for responsible AI use

Explore the key pillars of ethical AI, including fairness, reliability, privacy, transparency, sustainability, and explainability, and understand their importance in building responsible AI systems.

What you will learn (Refined):

• The six key pillars of ethical AI systems

• What each pillar represents and why it is important

• How these pillars help build trust and responsible AI usage

After completing this, you will be able to:

• Identify and explain the six AI ethics pillars

• Understand their role in responsible AI design

• Recognize how these principles impact real-world AI systems

Understand AI regulations, including key standards, their benefits in ensuring responsible AI usage, and the challenges involved in implementing them.

What you will learn:

• Key regulation standards and frameworks in AI

• Benefits of AI regulations for trust, safety, and compliance

• Challenges in implementing and enforcing AI regulations

After completing this, you will be able to:

• Understand the role of standards in AI governance

• Explain the benefits of AI regulations

• Identify challenges in regulating AI systems

Summarize the key concepts covered in the course, reinforce important takeaways, and understand the next steps to continue your AI learning and practical application journey.

What you will learn:

• Key concepts and topics covered throughout the course

• Important takeaways for applying AI in real-world scenarios

• Next steps to continue learning and using AI effectively

After completing this, you will be able to:

• Recall and summarize core AI concepts

• Apply key learnings in practical situations

• Plan your next steps in your AI learning journey