Introduction to Artificial Intelligence

• Understand the Similarities, Differences, and Collaboration between Human and Artificial Intelligence
• • Human Intelligence
  • What is Artificial Intelligence?
  • AI vs Human Intelligence
  • AI and Human Intelligence: Partners, Not Competitors
  • Human-AI Collaboration: Skills and Mindsets for Success
  • What is NOT AI?
  • Limitations of Current AI
• Explain How Data, Algorithms, and Models form the Foundation of AI Systems
• • Fundamental Concepts of AI
  • Role of Data and Algorithms in AI
  • Model: The Outcome of Learning
  • How AI Works Differently from Traditional Software
  • AI vs Traditional Software: Key Capabilities
• Summarize Major Milestones and Developments in the Evolution of AI
• • Early AI History
  • Modern AI History
  • Explore Recent Advancements and Future Directions Shaping AI Technologies
  • • Emerging Trends in AI
    • Technological Advancements Driving AI
    • The Road Ahead: Opportunities and Challenges
    • Module Summary

  Everyday AI Tools and Use cases

  • Identify Common AI Technologies Used in Daily Life
  • • Impact of AI in Daily Life
    • AI in Entertainment
    • AI as Personal Assistants
    • AI in Smart Homes
    • AI in Fitness
    • AI in Shopping and E-Commerce
    • AI in Customer Service
    • AI in Travel and Navigation
    • AI in Budgeting
  • Recognize AI Tools in the Workplace and How they Improve Workflow and Decision-Making
  • • AI: The Smart Work Companion
    • Optimize Workplace Productivity with AI
    • AI-Driven Collaboration in the Workplace
    • AI-Driven Decision-Making at Work
    • AI-Powered Financial Decision Support
    • Optimize Hiring and Job Search with AI
    • AI Tools in Workspace
  • Explain How AI Improves Manufacturing and Industrial Processes
  • • Smart Industry with AI
    • AI-Powered Predictive Maintenance of Machinery
    • Product Quality Inspection with AI
    • AI in Supply Chain Optimization
    • AI-Powered Cobots in Manufacturing
  • Explain How AI Improves Transportation Safety, Efficiency, and Sustainability
  • • Making Travel Smarter with AI
    • AI in Autonomous Vehicles
    • Smart Traffic Management with AI
    • Smart Logistics and Fleet Management
    • Safety and Collision Detection Systems
    • Disaster Management: Google AI for Wildfire Detection
    • Sustainability: John Deere’s AI for Precision Agriculture Renewable Energy Management using AI
  • Identify How AI Personalizes Learning and Provides Feedback
  • • AI in Education: Transforming the Future of Learning
    • AI-Driven Intelligent Tutoring Systems
    • AI-Driven Grading and Feedback Systems
    • AI-Driven Student Performance Analytics
    • AI-Driven Adaptive Learning Platforms
  • Identify How AI Improves Security by Detecting Threats, Protecting Data, and Ensuring Privacy
  • • Smarter Security Starts with AI
    • AI in Cybersecurity
    • AI for Data Privacy & Identity Verification
    • Module Summary

  Building Blocks of AI

  • AI and its Disciplines
• Understand the Role of Data for Effective AI Systems
  • Data
  • Importance of Data Quality for Effective AI
  • AI Data Categories and Origins
  • Types of AI Data
  • AI Data Flow Basics
  • Structured vs. Unstructured Datasets
  • Labeled vs. Unlabeled Datasets
  • Creating Datasets for AI Models
  • Ways to Sample Data
• Identify the Different AI Models and Explain How They are Developed and Trained
• • Key Features of AI Models
  • Types of AI Model
  • AI Model Development Process
  • How AI Models are Trained
  • Challenges in Training AI Models
  • Testing AI Models
  • Improving AI Models
  • Evaluating Model Performance
• Understand Machine Learning and Neural Networks
• • What is Machine Learning?
  • Machine Learning Algorithms
  • How Machine Learning Improves Decision-Making
  • Limitations of Machine Learning
  • Neural Networks
  • Layers, Nodes, and Weights in Neural Networks
  • Deep Learning (DL)
  • How DL Overcomes Limitations of ML
  • Working of DL
  • DL Algorithms
  • Computer Vision
• Understand Natural Language Processing (NLP) and its Role in AI
• • Natural Language Processing (NLP)
  • Why NLP is Important in AI
  • How NLP Processes Human Language
  • Processing Text for NLP Tasks
  • Key NLP Tasks
  • Sentiment Analysis in NLP
  • Text Summarization in NLP
  • Language Translation in NLP
  • Challenges in NLP
• Explain Generative AI (GenAI) and Large Language Models (LLMs)
• • What is Generative AI?
  • Traditional AI vs Generative AI
  • Foundation Models of Generative AI
  • Popular GenAI Tools
  • Large Language Models (LLMs)
  • Small vs. Large Language Models
  • Key Terms for GenAI and Language Models
• Understand Advanced AI Systems and Technologies
• • Robotics
  • Multimodal AI
  • AI Agents
  • Agentic AI
  • XAI (Explainable Artificial Intelligence)
  • Expert Systems
• Select the Appropriate Tools Based on AI Project Requirements
• • Select the Appropriate Tools for AI Projects
  • Understand Project Type
  • Consider Tool Characteristics
  • Recommended AI Tools by Use Case
  • Module Summary

Prompt Crafting for Effective AI Interactions

• Understand the Basics of Prompt Engineering
• • What is a Prompt?
  • How AI Responds to Prompts
  • What is Prompt Engineering?
  • Why is Prompt Engineering Important?
  • How does Prompt Engineering Work?
  • How Different AI Models Interpret Prompts
  • Comparison of AI Platform Responses
• Learn How to Craft Effective Prompts
• • Key Principles of Crafting Effective Prompts
  • Ask the Right Question
  • Make Prompts Clear
  • Make Prompts Specific
  • Make Prompts Relevant
  • Test and Refine Prompts
  • Handle Unsatisfactory Responses
  • Rephrasing Prompt
• Learn Prompt Engineering Techniques
• • Prompting Techniques for Written Projects
  • Prompting Techniques for Image or Video Project
  • Prompting Techniques for Multimodal AI
  • Chain of Thought (CoT) Prompting Techniques
  • Iterative Prompting Techniques
  • Managing Long Conversations
  • Module Summary

  AI Ethics and Responsible AI

  • AI Ethics and Responsible AI
• Identify Key Ethical, Societal, and Security Concerns in AI Systems
• • AI Concerns
  • AI Ethical Concern: Bias and Discrimination
  • AI Ethical Concern: Lack of Transparency
  • AI Ethical Concern: Accountability and Responsibility
  • AI Ethical Concern: Intellectual Property and Copyright Violations
  • Ethical Concerns Introduced by GenAI
  • Privacy and Security Concern: Privacy and Surveillance
  • Real-world Privacy and Data Protection Implications
  • Privacy and Security Concern: Cyber Attacks
  • Societal Concern: Job Displacement
  • Societal Concern: Mental Health Impact
  • Societal Concern: Hallucinations
  • Societal Concern: Misinformation and Deepfakes
  • Long-Term Concerns: Autonomous Weapons
  • Long-Term Concerns: Emergence of AGI
• Explain the Principles and Importance of Using AI Ethically and Fairly
• • What is Responsible AI?
  • Why Responsible AI Use Matters?
  • Using AI Responsibly in Daily Life
• Apply Responsible Practices, Governance, and Global Standards in AI Usage.
• • Maintain Accountability in AI Usage
  • Avoid Over-Reliance on AI
  • Configure Privacy Settings in AI Tools
  • Setting Up Privacy Controls in ChatGPT
  • Exercise Caution Sharing Personal Data with AI Tools
  • Managing AI App Permissions Effectively
  • Stay Updated on AI Policy Changes and News
  • Regularly Update and Audit AI Tools
  • Use AI Applications Ethically
  • Ethical AI Design: Key Considerations
  • Considerations for Navigating GenAI Ethical Challenges
  • Safeguard Against AI Security Risks
  • Regulation and Governance in AI
  • Responsible AI Global Initiatives
  • Legal Foundation of Responsible AI
  • AI Regulations in Action: GDPR, CCPA, and DPDP Act
  • Building a Responsible AI Future
  • Module Summary