Certification in Large Language Model (LLM) | Udemy

Take 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 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to LLMs

1.1. 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 LLMs

1.5. Activity: Explore LLMs through interactive sessions (e.g., ChatGPT, Bard, Claude).

1.6. Conclusion

Module 2. Core Technologies and Architectures of LLMs

2.1. Neural Networks and Deep Learning Basics

2.2. Attention Mechanisms and Transformers

2.3. Pre-training and Fine-tuning Paradigms

2.4. Tokenization and Contextual Embeddings

2.5. Popular LLM Architectures: GPT, BERT, T5, and PaLM

2.6. Activity: Visualize attention maps in transformers using tools like Hugging Face.

2.7. Conclusion

Module 3. Training and Scaling LLMs

3.1. Data Collection and Preprocessing for LLMs

3.2. Compute Requirements and Scaling Challenges

3.3. Model Optimization Techniques (e.g., mixed-precision training)

3.4. Distributed Training for LLMs

3.5. Overview of OpenAI GPT, Meta LLaMA, and Google PaLM Training Practices

3.6. Activity: Simulate a small-scale model training using libraries like TensorFlow or PyTorch.

3.7. Conclusion

Module 4. Applications of LLMs

4.1. Text Generation and Summarization

4.2. Chatbots and Virtual Assistants

4.3. Sentiment Analysis and Customer Insights

4.4. Question Answering Systems

4.5 Code Generation and Automation

4.6. Activity: Build a chatbot or text summarization tool using OpenAI’s API or Hugging Face models.

4.7. Conclusion

Module 5. Fine-Tuning and Customizing LLMs

5.1. Techniques for Fine-Tuning Pre-trained Models

5.2. Domain-Specific Adaptations of LLMs

5.3. Few-Shot and Zero-Shot Learning with LLMs

5.4. Case Study: Fine-Tuning for Healthcare, Legal, or E-Commerce Applications

5.5. Activity: Fine-tune a pre-trained LLM on a specific dataset using open-source tools.

5.6. Conclusion

Module 6. Deployment and Optimization of LLMs

6.1. Model Inference and Latency Optimization

6.2. Edge Deployment vs. Cloud Deployment

6.3. 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. Conclusion

Module 7. Ethical and Security Considerations

7.1. Bias, Fairness, and Responsible AI

7.2. Data Privacy Concerns and Mitigation

7.3. Risks of Misinformation and Misuse (e.g., deepfakes, fake news)

7.4. Regulations and Governance for LLMs

7.5. Activity: Analyze an ethical dilemma in LLM usage through group discussion.

7.6. Conclusion

Module 8. Future of LLMs

8.1 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 LLMs

8.4. Research Frontiers in LLMs

8.5. Activity: Research and present on the potential impact of LLMs in a specific field (e.g., education, healthcare).

8.6. Conclusion

Part 2

Capstone Project.