Generative AI and Large Language Models

About this course

This course offers a hands-on, beginner-friendly introduction to Generative AI and Large Language Models (LLMs). From foundational machine learning concepts to real-world NLP applications, learners will gain both theoretical knowledge and practical experience using Python and Hugging 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 AIBasic probability concepts and Bayes' theorem
• Case studies in digit recognition

Introduction to Large Language Models (LLMs)What LLMs are and what they can doReal-world applications of LLMsUnderstanding the language modeling challenge

Core Architectures Behind LLMsFully Connected Neural Networks and their role in MLRNNs 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 PythonPretraining vs Finetuning explained simply

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

Hands-On Labs and DemosLab 1: Build a chatbot using Hugging FaceLab 2: Perform sentiment analysis on text data<

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