Overview

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 dataLab 3: Create a simple translation modelLive Python demos on tokenization, embeddings, and inferencingEvaluation and Inference TechniquesBLEU and ROUGE scores for evaluating model outputsIn-context learning: zero-shot, one-shot, and few-shot examplesWho This Course Is For:Beginners in AI/ML looking for a practical introduction to LLMsDevelopers curious about how models like ChatGPT workStudents seeking a project-based approach to NLP and Generative AIAnyone interested in building their own language-based applications using open-source toolsThis course combines intuitive explanations, real-world demos, and hands-on labs to ensure you walk away with both confidence and competence in working with LLMs and Generative AI.

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