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To provide learners with a foundational understanding of Artificial Intelligence, its evolution, basic classifications, and its impact on the world around us. This module serves as the cornerstone for developing AI literacy in today’s rapidly evolving technological landscape. Students will explore core concepts, examine ethical considerations, and analyze real-world applications across diverse industries.
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Lesson 1: Introduction to Artificial IntelligenceTo provide learners with a foundational understanding of Artificial Intelligence, its evolution, basic classifications, and its impact on the world around us. This module serves as the cornerstone for developing AI literacy in today's rapidly evolving technological landscape. Students will explore core concepts, examine ethical considerations, and analyze real-world applications across diverse industries.
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Lesson 2: What is Artificial Intelligence?
AI systems vary in complexity from simple rule-based systems to sophisticated neural networks that can learn patterns from massive datasets. The most advanced systems today combine multiple approaches and modalities.
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Lesson 3: Introduction to Artificial IntelligenceWelcome to the very first video of our AI course! In this session, we step into the fascinating world of Artificial Intelligence. You will understand what AI actually is, how it has developed over time, and the different types of AI—Narrow AI, General AI, and Superintelligence. I will also explain how AI is different from Machine Learning and Deep Learning in simple and clear terms. We will explore real-life examples from everyday use, such as chatbots, Netflix recommendations, and voice assistants like Siri and Alexa. Towards the end, there is a small activity where you will classify common tools as AI or non-AI.
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Lesson 4: Brief History and Evolution of AIKey Concepts:
- Early ideas: Alan Turing and the Turing Test
- First AI programs in the 1950s–60s (Logic Theorist, ELIZA)
- AI winters and periods of optimism
- Rise of machine learning and neural networks in the 2000s
- AI today: Generative AI, NLP, and real-time systems
- Current paradigm shift from supervised to unsupervised learning
- The emergence of multimodal AI systems
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Lesson 5: Types of AIKey Concepts:
- Narrow AI (Weak AI): Performs a single task (e.g., Spotify recommendation, Google Search)
- General AI (Strong AI): Hypothetical – performs any intellectual task that a human can
- Superintelligent AI: Beyond human intelligence – still theoretical
- Machine Learning: Subset of AI where systems learn from data without explicit programming
- Deep Learning: Advanced form of machine learning using neural networks with multiple layers
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Lesson 6: AI vs Automation vs Robotics
Key Concepts
- Automation – Machines follow fixed rules to do repetitive tasks without thinking or learning.
- AI (Artificial Intelligence) – Technology that learns from data, makes decisions, and improves over time.
- Robotics – Robots are machines that can move and interact with the environment; they may or may not be smart.
- AI + Robotics – When robots use AI to sense, learn, and adapt, making them smarter and more useful in real-world tasks.
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Lesson 7: Difference between AI, Machine Learning, and Deep LearningKey Concepts:
- AI is a broad concept
- Machine Learning (ML): A Subset of AI focused on data-driven learning
- Deep Learning (DL): A Subset of ML using neural networks with multiple layers
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Lesson 8: Everyday examples: Chatbots, recommendation systems, personal assistants
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Lesson 10: Debunking Myths About AI
Key Concepts
- Common misconceptions
- Realities
- Research and Evidence
- Real life examples
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Lesson 11 : Societal Impact and Why Learn AIKey Concepts
- Positive Impact
- Why Learn AI
- Research and Evidence
- Real life examples
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Section Quiz
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Lesson 1: Key Concepts Behind AI SystemsTo understand the fundamental principles that power AI systems—algorithms, learning paradigms, data, models, training and testing, and evaluation—with conceptual clarity and hands-on activities.In this module, we'll dive deep into the building blocks that make artificial intelligence possible. You'll explore how algorithms serve as the logical foundation for AI systems, examine different learning paradigms like supervised and unsupervised learning, and understand the critical role data plays in training effective models. We'll demystify complex concepts through intuitive explanations and provide practical examples that illustrate how these components work together to create intelligent systems.
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Lesson 2: Youtube
Welcome back to the next video in our AI course! In this lesson, we go a bit deeper and explore the key concepts that drive AI systems. You will learn what algorithms and automation actually mean in the context of AI, and what makes an AI model different from a traditional program. We will talk about training data versus testing data, and introduce neural networks in a very simple, conceptual way—no maths involved! You will also get to know the three main learning types in AI: supervised learning, unsupervised learning, and reinforcement learning. To make things relatable, I will take you through real examples like how email spam filters work and how Netflix recommends movies to you.
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Lesson 3: Algorithms: The Brains of AIKey Concepts:
- Definition of an algorithm: A finite sequence of clear, step-by-step instructions to solve a problem.
- Importance: AI relies on algorithms to process data and make decisions.
- Characteristics: Well-defined inputs/outputs, finite steps, deterministic or probabilistic.
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Lesson 4 : Learning Paradigms in AIKey Concepts:Supervised LearningModel learns from labeled data (inputs + outputs). The AI is trained on a dataset where the correct answers are provided, allowing it to make predictions on new, unseen data. This is similar to learning with a teacher who provides immediate feedback. The system gradually improves by minimizing the difference between its predictions and the known correct answers.
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Lesson 5: Data: The Fuel of AIKey Concepts:
- Definition of a dataset: A collection of structured/unstructured data used to train and evaluate AI models. Structured data is organized in tables with rows and columns (like spreadsheets), while unstructured data includes images, text, audio, and video.
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Lesson 6: Training, Testing, and Model EvaluationKey Concepts:
- Splitting data into training (learn from) and testing (evaluate performance) sets is fundamental to AI development.
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Lesson 7: AI Models: How They Learn
Key Concepts:
- Definition of a model: A program that maps inputs to outputs based on data, creating patterns and relationships between variables. Models represent simplified versions of real-world systems or phenomena.
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Lesson 8: Ethics: Building Fair and Transparent AIEthics forms the foundation of responsible AI development. As AI systems increasingly impact our daily lives, understanding and addressing ethical considerations becomes essential for all AI practitioners. The rapid advancement and widespread adoption of AI technologies raise complex questions about values, rights, and societal impacts that must be addressed proactively rather than reactively.
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Lesson 9: Final Quiz for Module 2
This comprehensive assessment will evaluate your understanding of AI fundamentals covered in Module 2. The quiz consists of 10 in-depth questions requiring detailed explanations and critical analysis. You'll have 90 minutes to complete the assessment, which constitutes 25% of your module grade.
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Lesson 10: Capstone Task for Module 2Project: AI Concept MapCreate a comprehensive mind map (digital or hand-drawn) that synthesizes the key AI concepts covered in Module 2. Your concept map should serve as a visual representation of your understanding and demonstrate how different AI concepts interconnect with each otherYour concept map should include and show relationships between:
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