Stanford CS336 Language Modeling from Scratch

About this course

This Stanford course takes you inside the machinery of large language models — the AI systems behind ChatGPT, Claude, and other modern AI assistants. You'll build an LLM from the ground up, learning tokenization, neural network architecture, training mechanics, and alignment techniques that keep these models safe and useful. Stanford's computer science program is one of the world's most respected sources for AI education, and this course represents the real curriculum used to train AI researchers and engineers.

What you'll learn

• How to design and implement tokenization schemes that convert raw text into numbers that neural networks can process
• Build transformer architectures from scratch, understanding self-attention mechanisms and why they're crucial for language understanding
• Set up efficient training pipelines: data loading, gradient computation, and optimization strategies that work at scale
• Implement techniques to align language models with human values, including RLHF (Reinforcement Learning from Human Feedback)
• Debug and evaluate LLM behavior, measuring performance across benchmarks and real-world use cases
• Understand the computational trade-offs in model design — why bigger isn't always better, and how to think about efficiency
• Work with modern tools and frameworks used in industry: PyTorch, distributed training, and best practices from research papers

Who this is for

You're ready for this course if you're serious about understanding how AI actually works — not just using it, but building it. This isn't a surface-level overview; it's hands-on engineering work.

• AI researchers and PhD aspirants — gain the foundational knowledge expected in top AI labs and research groups, whether at universities or leading tech companies
• Machine learning engineers — learn the specific architectures and training techniques you'll encounter in production AI systems, directly applicable to jobs at AI startups and tech companies

Prerequisites

You'll need solid Python programming skills, linear algebra (vectors, matrices, basic derivatives), and familiarity with machine learning fundamentals like backpropagation. Some experience with PyTorch or TensorFlow is helpful but not required. If you're comfortable reading research papers and implementing algorithms, you're ready.

Why this matters for Indian learners

India's AI talent is in high demand globally and locally. Startups in Bangalore, Delhi, and Mumbai are hiring ML engineers and AI researchers at competitive salaries (₹15–40 LPA depending on experience), and this course teaches the exact skills they're looking for. Tech giants like Google India, Microsoft Research India, and homegrown companies like Flipkart and Ola are scaling AI teams. Understanding LLM internals positions you for roles that pay significantly more than general software engineering, and opens doors to consulting, research, or founding your own AI company.

Frequently asked questions

Is this course really free?

Yes. Stanford provides the full course materials, lectures, and assignments without charge. You can learn everything without paying anything.

How long will it take to complete?

The course is designed for 40 hours of total engagement. If you're balancing this with work or study, that's roughly 5–8 hours per week over 2–3 months, though you can move faster or slower depending on how deep you dive into each topic.

Will I get a certificate?

This course doesn't offer an official certificate of completion. However, completing the projects and building an LLM from scratch is something you can show employers and add to your portfolio — often more valuable than a certificate.