Cutting-Edge LLMs and Multimodal AI
Explore the Frontiers of Intelligence—Master LLMs and Multimodal AI
Early access to e-LMS included
About This Course
Cutting-Edge LLMs and Multimodal AI is an advanced-level program crafted for AI professionals, researchers, and developers who want to stay ahead in the rapidly evolving landscape of generative and multimodal intelligence. The course dives into the architecture, capabilities, and real-world applications of the latest LLMs (like GPT-4, Claude, Gemini, and LLaMA) and their integration with vision, audio, and sensor modalities to build powerful, human-like systems.
Aim
To provide in-depth knowledge and hands-on experience in advanced Large Language Models (LLMs) and multimodal AI systems that integrate text, image, speech, and video inputs for next-generation applications.
Program Objectives
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To advance learners’ understanding of modern LLM and multimodal architectures
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To equip them with hands-on skills for building and deploying real-world AI systems
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To explore use-cases across healthcare, law, media, and accessibility
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To cultivate ethical, responsible practices in frontier AI development
Program Structure
Week 1: Next-Gen LLMs – Capabilities, Architecture, and Trends
Module 1: Deep Dive into Modern LLMs
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Chapter 1.1: Evolution from GPT-3 to GPT-4, Claude, Gemini, and beyond
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Chapter 1.2: Transformer Enhancements (Mixture of Experts, Long-Context, LoRA)
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Chapter 1.3: Performance Benchmarks and Trade-offs
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Chapter 1.4: Open vs. Closed Models (Open-source innovations: LLaMA, Mistral, Mixtral)
Module 2: Advanced Prompting and Fine-Tuning
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Chapter 2.1: Structured Prompting Techniques (Zero/Few-shot, CoT, Tool-Use)
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Chapter 2.2: Retrieval-Augmented Generation (RAG) Overview
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Chapter 2.3: Fine-Tuning vs. Instruction Tuning vs. RLHF
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Chapter 2.4: Evaluation and Safety Alignment Metrics
Week 2: Foundations of Multimodal AI Systems
Module 3: Language + Vision Models
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Chapter 3.1: Multimodal Transformers (BLIP-2, Flamingo, GPT-4V, Gemini)
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Chapter 3.2: Vision Encoding and Alignment with Text Embeddings
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Chapter 3.3: Image Captioning, Visual Q&A, Scene Understanding
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Chapter 3.4: Visual Prompting, Layout Understanding, Image-to-Text Inference
Module 4: Language + Other Modalities
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Chapter 4.1: Audio-Language Systems (Whisper, AudioCraft, VALL-E)
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Chapter 4.2: Video-Language Interaction (Sora, Pika Labs, RunwayML)
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Chapter 4.3: Code + Text and Structural Models (Code LLMs, ReAct)
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Chapter 4.4: Multimodal Embeddings and Cross-Modal Retrieval
Week 3: Applications, Ethics, and Future Outlook
Module 5: Industrial Applications and Innovation
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Chapter 5.1: Multimodal AI in Search, Design, Robotics, and Healthcare
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Chapter 5.2: Tool-Use and API-Augmented Agents (Auto-GPT, OpenAgents, ReAct)
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Chapter 5.3: Agent Simulations, Planning, and Toolchains
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Chapter 5.4: Case Studies: Enterprise LLM Use and Multimodal Integrations
Module 6: Ethics, Policy, and the Frontier of AI
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Chapter 6.1: AI Hallucinations, Safety, and Guardrails
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Chapter 6.2: AI Copyright, Content Authenticity, and Watermarking
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Chapter 6.3: Regulation Trends and Global AI Policies
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Chapter 6.4: What’s Next: Multimodal General Intelligence and Open Challenges
Who Should Enrol?
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AI/ML practitioners, data scientists, software engineers, researchers
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Prior knowledge of Python, LLMs, and basic neural network concepts
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Ideal for professionals building AI tools and cross-modal applications
Program Outcomes
Participants will be able to:
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Implement and deploy LLMs in multimodal settings
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Integrate image, speech, and video with language models
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Evaluate and optimize performance of cutting-edge AI systems
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Design next-gen applications across sectors using GenAI
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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