Course Overview

Natural Language Processing (NLP) has evolved rapidly, transforming how machines understand and generate human language. At the forefront of this revolution are Large Language Models (LLMs), which power everything from chatbots to advanced AI systems. This beginner-friendly course takes you on a guided journey from the foundations of NLP to building and deploying your very own LLM-powered application.

What’s in this course?

This course provides a step-by-step introduction to NLP, the rise of transformer-based models, and the practical use of pre-trained LLMs like BERT, GPT, and Llama. Whether you're a student, developer, or enthusiast, you'll gain a practical understanding of how modern language models work and how to leverage them using Python.

Through engaging lectures, live demos, and real-world examples, you’ll learn:

• How NLP has evolved and the limitations of early rule-based methods

• The inner workings of transformer architecture

• Key differences between popular LLMs like BERT and GPT

• How to interact with LLMs using Hugging Face

• Prompt engineering techniques to guide model outputs

• How to integrate LLMs into real applications and optimize them for deployment

• Ethical challenges and the future scope of language models

• How to build and deploy your first LLM-powered chatbot using Flask

Special Note:

This course emphasizes hands-on implementation using Python and vs code. Every concept is paired with a hands-on demonstration, ensuring you not only understand the theory but also gain practical skills in using LLMs effectively.

Course Structure:

• Lectures
• Demos
• Real World Examples/Applications
• Capstone Project

Course Contents:

• Course Introduction
• Getting started with NLP and limitations of traditional approaches
• "Attention is All You Need"-Rise of Transformers
• Deep Dive into Transformer Architecture
• Familiarizing popularly used LLMs (Llama, BERT, GPT)
• LLM training vs Fine-tuning for custom tasks
• Importance of designing effective prompt design
• Zero-shot vs Few-shot learning
• Optimizing deployment cost and latency
• Bias, Toxicity and Fairness challenges
• Mitigating risks to enhance safety
• Future scope for LLMs
• Capstone project - Creating and Deploying your very first Local LLM model

By the end of this course, you’ll be confident in understanding and working with Large Language Models. You’ll be able to build, fine-tune, and integrate LLMs into real-world applications and responsibly navigate their ethical implications.