run llm.course --start

From Tokens to Transformers

A free, certification course on GenAI × LLM mastery.

Unlock the technology behind intelligent systems: NLP, large language models, transformers, RAG and agents. Three weeks of structured learning with weekly assignments and industry expert sessions, then one week to build a GenAI capstone.

@caciitg · Consulting & Analytics Club, IIT Guwahati
// about the course

Short. Daily. Hands-on.

Built on the same idea as Summer Analytics, the club's long-running machine learning course: a little every day beats a lot once.

  1. 01

    Why this course?

    There is no shortage of material on language models; there is a shortage of order. Most people who start stop at the videos. This course is arranged so that the thing you build each day is small enough to finish, and the thing you build each weekend is worth keeping.

    Rather than a tour of topics, it follows a single sentence through a model — tokenised, embedded, attended over, decoded — and then treats retrieval, fine-tuning and agents as the ways of changing what comes out.

  2. 02

    What does it cover?

    NLP foundations, large language models, transformers, RAG, AI agents and multi-agent systems — from understanding how they work to building with them.

    • Three weeks of structured learning: three 30-minute tasks every weekday
    • Weekly assignments and hands-on exercises
    • Industry expert sessions
    • One week dedicated to a practical GenAI capstone project
  3. 03

    Who we are

    The Consulting & Analytics Club at IIT Guwahati is the student group behind Summer Analytics, which in 2025 drew more than 23,000 registrations from over 80 countries. This course is run by the club's machine learning team, with content curated from public lectures and notebooks; the assignments and the capstone are ours.

// curriculum

Three weeks to learn, one week to build.

Weeks are released on Mondays. Resources for a released week are on the course dashboard.

Week 01 / 04 · up next28 Sep – 4 Oct

Sequence models, attention and the transformer

Why text is hard for neural networks, what attention fixed, and what a transformer block is actually doing.

embeddingssequence modelsself-attentionthe transformer block

Week 02 / 045 – 11 Oct

How models are trained, and how to use them

The lifecycle from pretraining to instruction tuning, then the practical half: decoding, prompting, local models, evaluation.

training lifecycledecodingpromptinglocal models + evaluation

releases 5 oct  ·  dashboard
Week 03 / 0412 – 18 Oct

Retrieval, fine-tuning and agents

The ways to make a general model work for you. Retrieval changes what it knows; fine-tuning changes how it behaves; agents let it act.

embeddings & similarityRAGfine-tuning with LoRAagents & multi-agent systems

releases 12 oct  ·  dashboard
Week 04 / 0419 – 26 Oct

GenAI capstone

No new material. One problem statement, released on the Monday, and a week to build against it and submit.

problem statementbuildsubmitshowcase

releases 19 oct  ·  dashboard
// schedule

Key dates.

Weeks are released on Mondays. Assignment deadlines are Sundays, 11:59 PM IST.

2026-09-28Week 1 released
2026-10-05Week 2 released
2026-10-12Week 3 released
2026-10-19Week 4 released
2026-10-26Final submissions close
// before you start

Who this is for, and what you need.

Who this is for

Anyone who wants to understand what powers modern GenAI, whether you are starting out or looking to go deeper into LLMs. No prior NLP or LLM knowledge is required.

Being comfortable in Python and having seen a machine learning model before will make the hands-on tasks smoother, but neither is a hard requirement.

What you need

  • A Google account — Colab and a free API tier
  • A Hugging Face account — models and datasets
  • A GitHub account — assignments are submitted as links
  • Basic Python — numpy, pandas, comfort in a notebook
  • Discord — where questions get answered

Nothing needs to be installed locally until the second week. No credit card is required for any of it.

// questions

Frequently asked.

Anything else: ask on Discord.

How much time does this really take?
About nine hours a week: seven and a half of daily tasks and one to two on the assignment. The optional material roughly doubles that, which is why it is optional.
Do I need to know NLP or LLMs already?
No. The first week starts from word embeddings and builds up to the transformer. Basic Python helps with the notebooks.
Do I need a GPU?
No. Everything runs on free Colab. The week-three fine-tune is sized to fit a free T4.
Does it cost anything?
No. Free API tiers and local models throughout, and registration is free.
I missed a few days. Should I catch up or move on?
Move on. Only the previous weekend's assignment is a hard prerequisite for the next week. Come back to the missed days later if you want to.
Can I use ChatGPT or Claude for the assignments?
Use them to understand things and to debug; that is what practitioners do. Do not submit generated code you cannot explain. The capstone is scored on a question set you never see, so it rewards understanding over copying.
// 2026 edition

Registrations open!

Free to participate · open to everyone · no prior NLP or LLM knowledge required.