Online Course on Large Language Models

Sitare University is offering a 6-month online course on Large Language Models [LLMs] that aims to provide a strong foundation for students as well as working professionals looking to venture into this field. There are a lot of courses that teach you how to use various python packages and AI tools, but this course focuses on the conceptual foundations of LLMs along with significant hands-on project practice. By the end of this course, you would have gained a deep understanding of the transformer architecture, how LLMs are trained, how they work, which LLM to use in which situation and also be able to build and deploy your own LLM based applications. In order to make the course affordable for you, the initial concept building will be in self-learning mode with regular doubt clearing sessions. And then each individual student will get a personalized set of LLM based hands-on projects to work on.

First 3 months of Concept Building Next 3 months of Hands-On Training
  • Guided self-learning of NLP and LLMs through videos and online resources

  • Regular doubt clearing on Slack/WhatsApp

  • Weekly 1hr online doubt clearing session

  • Regular Online Quizzes for revision and feedback
Hands-on LLM Project experience
  • Text Classification
  • Named Entity Recognition
  • Information Retrieval and RAG
  • Question Answering

Build LLM Apps using tools like LangChain


Certificate from Sitare University on successful completion of course.
Final evaluation based on Online Test and Project Presentation.
At least 15 hours per week of commitment expected.

Course Instructor

Dr. Kushal Shah (alumnus of IIT Madras and ex-faculty at IIT Delhi)

Course Fees

INR 20,000/-

Application Process

Apply anytime by filling this Course Application Form.
You can apply anytime of the year. Each student will have an independent learning path.
Online test to check prior Python and ML knowledge.
Selected students will be emailed the bank details for fee payment.

Weekly Concept Building Plan for First 12 Weeks

  1. Revision of ML Algorithms for Classification
  2. Word Tokenization and Sentence Segmentation
  3. Text Classification with classical ML algorithms
  4. Sub-Word Tokenization
  5. Word Embeddings
  6. Recurrent Neural Networks (RNN)
  7. Long Short Term Memory (LSTM) Network
  8. Transformers and the Attention Mechanism
  9. Inference using BERT-like models
  10. Inference using LLaMA and GPT like models
  11. Fine-Tuning of BERT for various tasks
  12. Fine-Tuning of LLaMA and GPT for various tasks

Frequently Asked Questions [FAQs]

  1. When does the course start?
    This is a personalised course, where everyone has their own trajectory. We will surely have common doubt clearing sessions, but apart from that its highly personalised. And so there is no common start date. You can start any time of the year.

  2. What if I don't know Python programming?
    Python is a must for this LLM course. You can start learning Python using these videos and practice problems.

  3. What if I have not learnt Machine Learning?
    You don't have to be an expert in ML, but some basic knowledge is needed. You can learn ML basics for free through the Self Shiksha portal and YouTube videos.

  4. What kind of references will be given for the self-learning component?
    The references will be a mix of videos and blogs. You can take a look at this Curated references for basic NLP to get an idea. Also, here is my LLM Blog that you can read to learn about some basic concepts and interesting practical applications of LLMs. There will be several other videos and python notebooks that will be shared with the course participants, most of which are not available in the public domain.

  5. Will this course help me in getting an internship or a job in the LLM domain?
    There are several job opportunities in the LLM domain and doing this course will certainly give you sufficient background to clear most of these internship and job interviews.


Dr. Kushal Shah
Professor, Computer Science
Sitare University.

WhatsApp: 9891262133