AI Foundations: A Practical Introduction for Beginners

Purpose

To provide learners with a foundational understanding of Artificial Intelligence, its core concepts, essential interaction skills, common techniques, and practical application through prompt engineering, enabling them to engage confidently and effectively with AI technologies.

Rationale

Artificial Intelligence is rapidly transforming industries, careers, and daily interactions. Understanding AI is no longer optional but essential for navigating the modern world. This course addresses the need for accessible, foundational AI knowledge, demystifying complex topics and empowering individuals to understand, interact with, and leverage AI tools effectively and responsibly, regardless of their technical background. It bridges the gap between AI hype and practical understanding, fostering AI literacy critical for personal and professional growth in an increasingly AI-driven environment.

Target Audience

This course is designed for individuals with little to no prior knowledge of Artificial Intelligence who seek a clear and practical introduction. This includes professionals across various fields wanting to understand AI’s impact, students exploring future career paths, lifelong learners curious about emerging technologies, and anyone aiming to use common AI tools (like chatbots and generative AI) more effectively and confidently. No programming experience is required.

Course Layout:

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Lesson 1: Definitions and Importance of AI

• Lesson Purpose: To define Artificial Intelligence (AI) in simple terms, differentiate key concepts like Machine Learning (ML), Deep Learning (DL), and Generative AI, and explain the crucial reasons why understanding AI is vital in today’s world.
• Key Topics Covered:
  • Defining Artificial Intelligence (Simulating Human Intelligence, Core Goal, AI as a Field of Study)
  • Key Concepts Within AI (Machine Learning: How it Works, Analogy, Common Approaches; Deep Learning: Neural Networks, Strengths, Analogy; Large Language Models & Generative AI)
  • Understanding the Relationships (AI > ML > DL)
  • The Importance Factor: Why AI Matters (Industry Reshaping, Career Opportunities, Empowerment, Navigating the Future, Effective Tool Usage, Driving Innovation)
  • AI in Action: Real-World Examples (Personalized Recommendations, Virtual Assistants, Smarter Customer Service)

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Lesson 2: Key Skills Required for AI Interaction

• Lesson Purpose: To identify and describe the core competencies needed to effectively understand, interact with, and utilize AI tools as a beginner, focusing on practical interaction skills over deep technical coding.
• Key Topics Covered:
  • Skill 1: Prompt Engineering (Definition, Importance, Key Techniques: Zero-shot, One-shot, Few-shot, Chain of Thought, Practical Examples)
  • Skill 2: Understanding Fundamental AI Concepts (Concept Clarity, AI Hierarchy Review: AI, ML, DL, LLMs, Generative AI)
  • Skill 3: Basic Data Literacy (Role of Data in AI, Labeled vs. Unlabeled Data, Impact of Data Quality, Quantity, and Bias)
  • Skill 4: Problem-Solving & Analytical Thinking (Critical Evaluation of AI Outputs, Problem Decomposition, Recognizing Limitations & Hallucinations, Human Judgment)
  • Skill 5: Continuous Learning & Adaptability (Need for Lifelong Learning, How to Stay Updated, Engaging with AI Communities)
  • Note on Technical Skills (Distinction between using and developing AI)

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Lesson 3: Common AI Techniques Explained

• Lesson Purpose: To explain common techniques used within AI, particularly Machine Learning (Supervised and Unsupervised Learning) and Prompt Engineering, providing a clearer picture of how AI systems learn, operate, and are interacted with.
• Key Topics Covered:
  • Technique 1: Prompt Engineering (Review: How it Works, Benefits, When to Use for LLM interaction)
  • Technique 2: Supervised Learning (How it Works, Role of Labeled Data, Common Tasks: Classification & Regression, Benefits, When to Use)
  • Technique 3: Unsupervised Learning (How it Works, Learning Without Labels, Common Tasks: Clustering, Dimensionality Reduction, Anomaly Detection, Benefits, When to Use)
  • Differentiating Techniques (Based on data and goals)

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Lesson 4: Step-by-Step Guide to Implementing Prompt Engineering

• Lesson Purpose: To provide a clear, actionable, step-by-step guide for implementing effective prompt engineering when interacting with Large Language Models (LLMs), enabling learners to get significantly better and more relevant results from AI tools.
• Key Topics Covered:
  • Prompt Engineering Refresher
  • The 4-Step Prompt Engineering Process:
    • Step 1: Clearly Define Your Goal (Objective, Scope, Output Format)
    • Step 2: Provide Relevant Context (Implied Information, Role/Persona Assignment)
    • Step 3: Select and Apply Prompting Techniques (Zero-shot, One/Few-shot, Chain of Thought, Tool/Agent Prompting)
    • Step 4: Evaluate and Refine the Output (Critical Analysis, Iteration, Experimentation)
  • Tips and Best Practices for Effective Prompting
  • Potential Challenges (Generic Output, Bias, Hallucinations, Over-Reliance, Nuance)
  • Practical Application Exercise (Applying the 4 steps to a chosen task)