CMU 11-711 Advanced NLP

About this course

This advanced NLP course from Carnegie Mellon University teaches you how modern language models actually work — from tokenization and attention mechanisms to alignment and evaluation. Taught by Graham Neubig, it's a deep dive into the foundations that power tools like ChatGPT, covering both the theory and the practical decisions that shape how LLMs behave. CMU's computer science program is globally recognized, and this course represents cutting-edge instruction in one of AI's most important domains.

What you'll learn

• How tokenization shapes model input and why different tokenization strategies affect performance
• The attention mechanism — what it is, why it matters, and how to reason about what models "pay attention to"
• How modern LLMs are trained, including key concepts in pretraining and fine-tuning
• Alignment techniques — methods for steering model outputs toward desired behaviors
• Evaluation frameworks for NLP systems — metrics, benchmarks, and when they actually tell you something useful
• Practical debugging skills for language models — identifying failure modes and understanding why they happen
• How to read and critically engage with recent NLP research papers

Who this is for

You belong in this course if you've built basic NLP projects before and want to understand the "why" behind modern approaches. This is for people ready to engage with research-level material and prefer depth over breadth.

• AI researchers and engineers — deepen your technical foundation and stay current with LLM advances reshaping the field
• Machine learning practitioners — move beyond tutorials and understand the principles behind the tools you use daily

Prerequisites

You'll need solid Python programming skills, comfort with linear algebra and probability, and prior exposure to machine learning basics. You should understand neural networks and how backpropagation works. This course assumes you've completed introductory NLP or machine learning coursework.

Why this matters for Indian learners

India's AI and ML job market is expanding rapidly, with companies like Flipkart, Amazon India, Google India, and local AI startups building cutting-edge NLP systems for everything from Hindi language processing to customer service automation. Professionals who understand LLMs at this depth command premium salaries — mid-level NLP engineers in India's major tech hubs earn ₹20–40 lakhs annually, with senior roles reaching significantly higher. This course gives you the conceptual toolkit that separates engineers who implement off-the-shelf models from those who truly understand and innovate with them.

Frequently asked questions

Is this course really free?

Yes. CMU makes the course materials, lectures, and assignments freely available to everyone. You can learn everything without paying anything.

How long will it take to complete?

The course is structured as a 40-hour commitment. If you pace it across 10 weeks, that's roughly 4 hours per week — manageable alongside other work, though some weeks with heavy problem sets may ask for more. Most learners benefit from spending extra time on papers and projects beyond the base hours.

Will I get a certificate?

No formal certificate is offered by CMU for completing this course. However, you'll build a portfolio of projects and deep knowledge that matters far more to employers than a certificate — and you can showcase what you learned in job interviews and on your resume.