Description
🧠 Module 1: Foundations of AI and Intelligent Systems
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What is Artificial Intelligence? Scope and applications
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Differentiating AI, Machine Learning, and Deep Learning with real-world use cases
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Generative AI: How tools like ChatGPT, DALL·E, and Midjourney work
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Real-World Productivity with AI: ChatGPT for writing/code, Notion AI for task management, Bard/Copilot for research
📐 Module 2: Mathematical Foundations for AI
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Refresher: Algebra, equations, exponents, functions
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Vectors and matrices: operations, transformations, applications in ML
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Coordinate Geometry and distance measures
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Intro to Linear Algebra: dot product, matrix multiplication, identity matrices
🤖 Module 3: How Machines Learn
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Neural Networks: What they are and how they mimic the brain
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Layers, neurons, weights, and activation functions
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Forward propagation walkthrough
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Gradients and backpropagation simplified
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Understanding loss functions, cost minimization, and optimization with examples (like SGD)
🔍 Module 4: Exploring AI in Action
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Hands-on: Try AI demos (face detection, object detection using OpenCV or Mediapipe)
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Play with AI image generation (DALL·E, Stable Diffusion, Playground AI)
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Use Text-to-Speech (TTS) and Speech Recognition tools (Whisper, Google STT)
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Create avatars or voices with tools like Ready Player Me, ElevenLabs, or Synthesia
💻 Module 5: Getting Started with AI Programming – PyTorch Basics
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What is PyTorch? Why is it used in AI/ML research?
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Install and set up a Python + PyTorch environment (locally or with Google Colab)
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Understanding Tensors and how they represent data
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Build a basic neural network from scratch
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Train the model on sample datasets (like MNIST)
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Track accuracy, loss, and visualize results using
matplotlib
🏁 Project: Build and Present Your First AI Mini Project
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Ideas: Digit Recognizer, AI-based Calculator, Face Detection App, Art Generator
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Use modular thinking: input, model, output
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Train or use pre-trained models
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Present code, working demo, and your learnings