Generative AI and Large Language Models

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 AIWhat is ML with real-world examplesGenerative vs Discriminative AIBasic probability concepts and Bayes' theoremCase studies in digit recognitionIntroduction to Large Language Models (LLMs)What LLMs are and what they can doReal-world applications of LLMsUnderstanding the language modeling challengeCore Architectures Behind LLMsFully Connected Neural Networks and their role in MLRNNs and their limitations in handling long sequencesTransformer architecture and its advantagesKey components: Tokenization, Embeddings, and Encoder-Decoder modelsUnderstanding Key Concepts in TransformersSelf-Attention mechanism and QKV matricesTokenization and embedding demo in PythonPretraining vs Finetuning explained simplyInference tuning parameters: top-k, top-p, temperatureHands-On Labs and DemosLab 1: Build a chatbot using Hugging FaceLab 2: Perform sentiment analysis on text data<