Overview

DescriptionTake the next step in your AI journey! Whether you are an aspiring AI engineer, a developer, a creative professional, or a business leader, this course will equip you with the knowledge and practical skills to understand, implement, and apply Large Language Models (LLMs). Learn how state-of-the-art architectures like GPT, BERT, T5, and PaLM are reshaping industries from content creation and customer support to automation and intelligent systems.Guided by real-world examples and hands-on exercises, you will:Master the core concepts of LLMs, including deep learning foundations, Transformer-based architectures, and model training techniques.Gain hands-on experience building and fine-tuning LLMs using Hugging Face, OpenAI APIs, TensorFlow, and PyTorch.Explore applications of LLMs in chatbots, virtual assistants, summarization, question answering, and automation.Understand the ethical challenges and governance issues surrounding LLMs, from bias mitigation to data privacy.Position yourself for future opportunities by learning about the latest innovations and emerging trends in the LLM ecosystem.The Frameworks of the Course· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises- designed to help you deeply understand LLM architectures, practical applications, and real-world use cases.· The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on labs to deepen your understanding of Large Language Model.· In the first part of the course, you'll learn the fundamentals of AI, NLP, and the evolution of language models.· In the middle part of the course, you will develop a strong foundation in core LLM architectures (Transformers, GPT, BERT, T5, PaLM) along with real-world hands-on experiments.· In the final part of the course, you will explore ethical issues, deployment practices, future trends, and career paths in LLMs. All your queries will be addressed within 48 hours with full support throughout your learning journey.Course Content:Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to LLMs1.1. Overview of Artificial Intelligence and Natural Language Processing (NLP)1.2. Evolution of Language Models (from N-grams to Transformers)1.3. What Are Large Language Models?1.4. Key Features and Capabilities of LLMs1.5. Activity: Explore LLMs through interactive sessions (e.g., ChatGPT, Bard, Claude).1.6. ConclusionModule 2. Core Technologies and Architectures of LLMs2.1. Neural Networks and Deep Learning Basics2.2. Attention Mechanisms and Transformers2.3. Pre-training and Fine-tuning Paradigms2.4. Tokenization and Contextual Embeddings2.5. Popular LLM Architectures: GPT, BERT, T5, and PaLM2.6. Activity: Visualize attention maps in transformers using tools like Hugging Face.2.7. ConclusionModule 3. Training and Scaling LLMs3.1. Data Collection and Preprocessing for LLMs3.2. Compute Requirements and Scaling Challenges3.3. Model Optimization Techniques (e.g., mixed-precision training)3.4. Distributed Training for LLMs3.5. Overview of OpenAI GPT, Meta LLaMA, and Google PaLM Training Practices3.6. Activity: Simulate a small-scale model training using libraries like TensorFlow or PyTorch.3.7. ConclusionModule 4. Applications of LLMs4.1. Text Generation and Summarization4.2. Chatbots and Virtual Assistants4.3. Sentiment Analysis and Customer Insights4.4. Question Answering Systems4.5 Code Generation and Automation4.6. Activity: Build a chatbot or text summarization tool using OpenAI's API or Hugging Face models.4.7. ConclusionModule 5. Fine-Tuning and Customizing LLMs5.1. Techniques for Fine-Tuning Pre-trained Models5.2. Domain-Specific Adaptations of LLMs5.3. Few-Shot and Zero-Shot Learning with LLMs5.4. Case Study: Fine-Tuning for Healthcare, Legal, or E-Commerce Applications5.5. Activity: Fine-tune a pre-trained LLM on a specific dataset using open-source tools.5.6. ConclusionModule 6. Deployment and Optimization of LLMs6.1. Model Inference and Latency Optimization6.2. Edge Deployment vs. Cloud Deployment6.3. Introduction to Model Compression Techniques (e.g., pruning, quantization)6.4. APIs and Frameworks for LLM Deployment (OpenAI API, Hugging Face, TensorFlow Serving)6.5. Activity: Deploy a fine-tuned model via an API and test its performance.6.6. ConclusionModule 7. Ethical and Security Considerations7.1. Bias, Fairness, and Responsible AI7.2. Data Privacy Concerns and Mitigation7.3. Risks of Misinformation and Misuse (e.g., deepfakes, fake news)7.4. Regulations and Governance for LLMs7.5. Activity: Analyze an ethical dilemma in LLM usage through group discussion.7.6. ConclusionModule 8. Future of LLMs8.1 Advances in Multimodal Models (e.g., GPT-4 Vision)8.2. Emerging Trends in LLM Efficiency (e.g., sparse models, memory-efficient architectures)8.3. Cross-Disciplinary Applications of LLMs8.4. Research Frontiers in LLMs8.5. Activity: Research and present on the potential impact of LLMs in a specific field (e.g., education, healthcare).8.6. ConclusionPart 2Capstone Project.

Skills

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