Introduction to Large Language Models: IIT Madras YouTube Course

Large Language Models (LLMs) like GPT, LLaMA and PaLM are transforming the world of artificial intelligence. From powering chatbots to enabling advanced reasoning and content generation, these models are now at the heart of modern AI systems.

To help learners build a strong foundation, the IIT Madras B.S. Degree Programme has released a 55-part YouTube playlist, “Introduction to Large Language Models.” This free, comprehensive course explains the concepts behind LLMs starting from the basics of transformers and self-attention to advanced topics like fine-tuning, fast attention mechanisms and scaling strategies.

This blog provides a structured overview of the course, its key topics and why it’s one of the most valuable resources for anyone serious about AI.

Why Learn Large Language Models?

Before diving into the course, it’s important to understand why LLMs are so critical today:

Foundation of Modern AI – LLMs are the backbone of systems like ChatGPT, Gemini and Claude.
Wide Applications – From healthcare to finance, education to entertainment, LLMs are being deployed across industries.
Cutting-Edge Research – Understanding LLMs helps students and professionals contribute to new breakthroughs.
Career Growth – Roles in AI engineering, NLP research and applied data science increasingly require LLM expertise.

The IIT Madras course makes these concepts accessible in a structured, academic way, ideal for students, developers and enthusiasts.

Course Breakdown: 55 Videos, Step by Step

The playlist covers the entire journey of how large language models work. Here’s a structured summary:

1. Fundamentals of Transformers

Introduction to Transformer Architecture
Attention Is All You Need
Self-Attention & Multi-Head Attention
Masked Attention & Teacher Forcing
Positional Encoding (Sinusoidal, Rotary, ALiBi, NoPE)

2. Language Modeling Basics

Introduction to Language Modeling
Causal Language Modeling
Transformers for Language Modeling
Generative Pre-trained Transformer (GPT)
Decoding Strategies: Beam Search, Top-K, Top-P Sampling

3. Pre-training & Fine-Tuning

Pre-training Objectives
Next Sentence Prediction
Masked Language Modeling (BERT)
Adapting to Downstream Tasks
Fine-Tuning Strategies

4. Tokenization Techniques

Challenges in Tokenization
Byte Pair Encoding (BPE)
WordPiece Tokenizer
SentencePiece Tokenizer

5. Advanced Architectures & Experiments

BART Overview
GPT-2 and Prompting
Effect of Pre-training Dataset Size
Choices That Affect Model Performance
Experimenting with Objectives and Architectures

6. Efficiency & Scaling

Fast Attention Mechanisms
Sparse Attention & Low-Rank Approximation
Fast Inference Mechanisms
Scaling Laws and Road Ahead

By the end of the course, learners gain not just theoretical knowledge but also insight into real-world challenges faced when training and deploying LLMs.

Key Highlights

Instructor-Led by Experts – Delivered by IIT Madras professors, including Mitesh M. Khapra, a leading researcher in NLP.
Comprehensive Curriculum – Covers everything from the Transformer paper to cutting-edge methods like ALiBi and NoPE positional encodings.
Free & Accessible – Available to everyone on YouTube, making advanced AI education more inclusive.
Research-Oriented – References state-of-the-art papers and experimental results.

Final Thoughts

The IIT Madras “Introduction to Large Language Models” course is one of the most comprehensive and accessible playlists available today. Whether you’re a student building foundational knowledge, a developer learning transformers for real-world applications or a researcher diving into the nuances of attention and scaling, this course has something for you.

By the end, you’ll understand how models like GPT and BERT work under the hood, why tokenization is critical how fine-tuning shapes downstream tasks and what lies ahead in the future of LLMs.

👉 Watch the full course here: Introduction to Large Language Models – IIT Madras

External Resources

To dive deeper into related material, here are some helpful links: