LLM in Multimodal: Text, Vision, Audio, Video
Table of Contents
🌍 Why this course exists
The world is entering the multimodal intelligence era.
AI can now:
- 👀 see images
- 👂 hear audio
- 🎥 understand video
- 📄 reason over documents
- 🧠 think across modalities
But power without understanding is dangerous.
This course exists to:
- rebuild multimodal LLMs from first principles
- empower learners to build, not just use
- place ethics and humanity at the center
🎯 Learning outcomes
By the end of this course, learners will be able to:
- Explain what multimodal LLMs really are
- Design multimodal pipelines from scratch
- Choose between pretraining, fine-tuning, RAG, and agents
- Implement basic multimodal models in code
- Evaluate models correctly (not just accuracy)
- Share models responsibly with the global community
🧩 Modalities covered
- Audio ↔ Text
- Image ↔ Text
- Image ↔ Video
- Documents & QA
- Agents & RAG
- Human-in-the-loop systems
🧠 Course philosophy
We do not build AI to replace humans.
We build AI to help humans become better humans.
🧩 Course structure
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Lecture 01 — What Is a Multimodal LLM, Really?
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Lecture 02 — How to Think Like a Multimodal System Designer
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Lecture 03 — Training Paradigms: Pretraining, Fine-tuning, and Training from Scratch
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Lecture 04 — Audio ↔ Text ↔ Reasoning: Teaching Machines to Listen
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Lecture 05 — Image ↔ Text: Teaching Machines to See and Reason
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Lecture 06 — Video–Text Multimodal Intelligence
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Lecture 07 — Visual Question Answering (VQA) & Document Question Answering (DocQA)
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Lecture 08 — RAG, AI Agents & Agentic Multimodal Systems
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Lecture 09 — Evaluation of Multimodal & Agentic AI Systems
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Lecture 10 — Bias, Ethics & Human-in-the-Loop (HITL) in Multimodal AI
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Lecture 11 — Sharing Your Multimodal Model with the World (Hugging Face)
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Lecture 12 — Encoder, Decoder, and the Truth About How LLMs Are Trained
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Lecture 13 — Real-World LLM Engineer & Research Scientist Interview (Top Tech Level)
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Lecture 14 — Deep Learning Foundations & Modern AI (Final Mastery)