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Introduction to AI and ML

This introductory course on AI/ML will teach you the fundamentals of these rapidly evolving fields, including their definitions, key components, differences, types, applications, and ethical...

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$1,995 USD
Course Code WA3408
Duration 1 day
Available Formats Classroom, Virtual

This introductory course on AI/ML will teach you the fundamentals of these rapidly evolving fields, including their definitions, key components, differences, types, applications, and ethical implications. You will also learn how to analyze real-world use cases and applications of AI/ML.

Skills Gained

  • Define AI and ML and explain their key components and differences
  • Identify and discuss the types and applications of AI/ML
  • Summarize the basic concepts of machine learning
  • Analyze real-world use cases and applications of AI/ML
  • Evaluate the ethical implications of AI and develop strategies for building trust in AI systems

Course Details

Outline

Introduction

  • Course Objectives and Overview
  • Brief History of AI and ML

Fundamental Concepts of Artificial Intelligence (AI)

  • Definition and Components of AI
  • Rule-based systems
  • Learning systems
  • Types of AI
  • Narrow AI vs. General AI
  • Reactive, Limited Memory, Theory of Mind, Self-aware AI
  • AI vs Human Intelligence: Key Differences

Introduction to Machine Learning (ML)

  • What is ML and Why it Matters
  • Relationship between AI and ML
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Deep Learning
  • Basic Concepts in ML
  • Training data
  • Model
  • Prediction

Real-world Use Cases and Applications of AI/ML

  • Everyday AI Applications
  • Smart Assistants (e.g., Siri, Alexa)
  • Recommendations (e.g., Netflix, Amazon)
  • Healthcare
  • Diagnosis and Imaging
  • Personalized Medicine
  • Transportation
  • Autonomous Vehicles
  • Route Optimization
  • Finance
  • Fraud Detection
  • Robo-advisors
  • Entertainment
  • Music and Movie Creation
  • Game AI
  • Agriculture, Environment, and More

Trustworthy AI and Ethics

  • Importance of Trust in AI
  • Principles for Trustworthy AI
  • Transparency
  • Fairness
  • Security
  • Accountability
  • Real-world Issues and Controversies
  • Bias in AI
  • Job Displacement
  • Surveillance and Privacy
  • Strategies for Building Trust in AI Systems
  • Data Quality and Integrity
  • Clear Communication with Users
  • Inclusive Design and Testing
  • Role of Regulation and Governance
  • Group Activity: Discussing Ethical Scenarios

Preparing for an AI-Driven Future

  • Understanding AI’s Potential Impact on Society
  • Building AI Awareness in Various Fields
  • Embracing Continuous Learning and Adaptation
  • Interactive Discussion: How to Stay Updated and Involved

Conclusion and Recap

  • Key Takeaways
  • Further Resources and Learning Pathways
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