Introduction to Large Language Models (LLMs)

video Introduction to Large Language Models (LLMs)

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Course facts
  • Payment: Free to learn, exam paid
  • Credits: UGC / AICTE Credit
  • Learning style: Scheduled, fixed dates
  • Format: Video Based
  • Video access: Streaming Only
SWAYAM details
  • Semester: July 2026
  • Credits: 4
  • Course type: Elective
  • NCRF level: 4.5, 8.0
  • Faculty development: AICTE approved FDP
Created byIIT Delhi
Available onSWAYAMSWAYAM

Course Details
Learning Style

Scheduled/Semester
Level

All Levels
Study Mode

100% Online
Duration

Medium (10 – 40 Hours)
Languages
Audio Language

English
Outcomes
Academic Credits

UGC / AICTE Credit
Certificate

University Certificate
Price & Terms
Pricing Model

Free Content (Proctored Exam)
Technical
Content Medium

Video Based
Video Access

Streaming Only
Specification
Access

Free Access
Academic Level

Undergraduate/Postgraduate
Introduction to Large Language Models (LLMs)
SKU: introduction-large-language-models-llms Category: Tag: Brand:

Learn how large language models work: language modeling, Transformers in PyTorch, pre-training, prompting, alignment, retrieval and efficient tuning.

At a glance
Duration12 Weeks
LevelAll Levels
CertificateUniversity Certificate
LanguageEnglish
Taught byProf. Soumen Chakraborti + 1 other
AccessFree access
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Large language models sit on a stack of earlier ideas about modeling text, and the lectures build that stack in order. Statistical language models and deep learning basics come first, then word representations, neural language models, sequence-to-sequence models and attention. You implement a Transformer in PyTorch, compare pre-training strategies, and move on to prompting, alignment with human feedback, knowledge retrieval and parameter-efficient fine-tuning.

What You Will Achieve

  • Build and evaluate statistical and neural language models
  • Implement a Transformer in PyTorch and explain self-attention and positional encoding
  • Compare instruction tuning, prompting and parameter-efficient fine-tuning as ways to adapt a pre-trained model
  • Recognize hallucination, bias and toxicity risks in LLM output

Program Overview

Language Models Before Transformers

The statistical and neural groundwork that modern LLMs build on.

  • NLP pipeline and statistical language models
  • Deep learning and PyTorch basics
  • Word representation and tokenization strategies
  • Neural and sequence-to-sequence models with attention

Transformers, Pre-Training and Prompting

How today’s models are built, pre-trained and steered.

  • Self and multi-head attention, positional encoding
  • Implementing a Transformer in PyTorch
  • Encoder-only, encoder-decoder and decoder-only pre-training
  • Instruction tuning, prompting and alignment with human feedback

Knowledge, Efficiency and Responsible LLMs

Research directions that make models better informed, cheaper and safer.

  • Knowledge graphs for retrieval
  • Parameter-efficient fine-tuning, quantization, pruning and distillation
  • Interpretability and the residual stream view
  • Bias, toxicity and responsible LLMs

Certificate Awarded (Paid Exam)

Pass the proctored exam to earn it. The course is free to study; the certificate requires a paid, proctored exam.

Job Outlook

Language model skills are used in NLP research, machine learning engineering, and teams that build search, question answering and other text applications on pre-trained models.

Our take

Good for: Undergraduate and postgraduate students in computer science, electronics, IT or mathematics who want to understand how LLMs are built and studied.

Keep in mind: The final week surveys named models such as GPT-4 and Llama-3, which newer releases will overtake; the weeks on how models work date far less.

Bottom line: A research-oriented account of how LLMs work, with a Transformer you code yourself and a closing look at alignment and responsible use.

Frequently Asked Questions

Do I write code in this course?

Yes. PyTorch is introduced early and you implement a Transformer with it; HuggingFace is introduced alongside the pre-training lectures.

Does the course cover alignment and RLHF?

Yes. Alignment of language models, including alignment with human feedback (RLHF), follows the lectures on instruction tuning and prompting.

Which model compression methods are taught?

Quantization, pruning and distillation, in the same week as parameter-efficient fine-tuning methods such as LoRA.

Which word representation methods are taught?

Word2Vec, fastText and GloVe, followed by tokenization strategies, before the lectures turn to neural language models.

Videos: Introduction to Large Language Models (LLMs)

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    Introduction to Large Language Models (LLMs)
    Introduction to Large Language Models (LLMs)

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