Generative AI and Large Language Models

A beginner-friendly guide to Generative AI and LLMs covering transformer basics, and hands-on python labs

What you'll learn
- Understand the Fundamentals of Machine Learning and Generative AI
- Gain Practical Knowledge of Large Language Models (LLMs)
- Perform Hands-on Tasks Using Python and Hugging Face
- Evaluate and Tune LLM Outputs Effectively

Requirements
- No programming experience needed

Description
This course offers a hands-on, beginner-friendly introduction toGenerative AIandLarge Language Models (LLMs). From foundational machine learning concepts to real-world NLP applications, learners will gain both theoretical knowledge and practical experience usingPythonandHugging Face.

By the end of the course, you will understand how LLMs work, how they are built, and how to apply them to real-world problems like chatbots, sentiment analysis, and translation.

What You'll Learn:

Foundations of Machine Learning (ML) and Generative AI

What is ML with real-world examples

Generative vs Discriminative AI

Basic probability concepts and Bayes' theorem

Case studies in digit recognition

Introduction to Large Language Models (LLMs)

What LLMs are and what they can do

Real-world applications of LLMs

Understanding the language modeling challenge

Core Architectures Behind LLMs

Fully Connected Neural Networks and their role in ML

RNNs and their limitations in handling long sequences

Transformer architecture and its advantages

Key components: Tokenization, Embeddings, and Encoder-Decoder models

Understanding Key Concepts in Transformers

Self-Attention mechanism and QKV matrices

Tokenization and embedding demo in Python

Pretraining vs Finetuning explained simply

Inference tuning parameters: top-k, top-p, temperature

Hands-On Labs and Demos

Lab 1: Build a chatbot using Hugging Face

Lab 2: Perform sentiment analysis on text data

Lab 3: Create a simple translation model

Live Python demos on tokenization, embeddings, and inferencing

Evaluation and Inference Techniques

BLEU and ROUGE scores for evaluating model outputs

In-context learning: zero-shot, one-shot, and few-shot examples

Who This Course Is For:

Beginners in AI/ML looking for a practical introduction to LLMs

Developers curious about how models like ChatGPT work

Students seeking a project-based approach to NLP and Generative AI

Anyone interested in building their own language-based applications using open-source tools

This course combinesintuitive explanations,real-world demos, andhands-on labsto ensure you walk away with both confidence and competence in working with LLMs and Generative AI.

Who this course is for:
- Beginner AI Aspirants
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