Artificial Intelligence Essentials (AIE)

Price
$995.00 USD

Duration
2 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Business Professionals (Marketing, Finance, Operations), Project Managers, HR / L&D Professionals, IT Professionals (non-AI specialists), Students / Early Career Professionals

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Course Prerequisites

No formal prerequisites, Designed as a foundational / entry-level course, Non-technical professionals or Beginners

Agenda

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

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