Certification in Large Language Model (LLM)

Learn concepts and architectures behind LLMs, GPT, BERT, T5, and PaLM, Training, Scaling Applicatios, deployment of LLM

What you'll learn
- ou will learn about the Introduction to LLMs including fundamentals of Artificial Intelligence and Natural Language Processing (NLP)
- Understand what makes Large Language Models (LLMs) unique in today’s AI landscape. You will also explore the key features and real-world capabilities of LLMs,
- You will develop a solid understanding of the core concepts and architectures behind LLMs, beginning with the basics of neural networks and deep learning.
- You will explore the role of attention mechanisms and study the Transformer architecture, which underpins most modern LLMs
- Learn how tokenization and contextual embeddings work, and you’ll study popular architectures like GPT, BERT, T5, and PaLM in detail
- You will gain in-depth knowledge of Training and Scaling LLMs. You will explore how large datasets are collected and preprocessed
- You will study model optimization techniques, such as mixed-precision training, and learn how distributed computing enables the training of very large models
- You will review real-world training practices behind advanced LLMs like OpenAI GPT, Meta LLaMA, and Google PaLM
- You will learn about the Applications of LLMs across different industries, including text generation, summarization, chatbot creation, virtual assistants
- Learn , sentiment analysis, customer insights, question answering systems, code generation, and automation.
- You will master the process of fine-tuning and customizing LLMs to fit specific domains. You will study the techniques behind adapting pre-trained models
- Work on real-world case studies including healthcare, legal, and e-commerce use cases. You will also fine-tune a pre-trained LLM
- You will explore the strategies for the deployment and optimization of LLMs, including best practices for model inference, reducing latency
- You will also learn about model compression techniques such as pruning and quantization, and explore various APIs and frameworks like OpenAI API, Hugging Face
- You will understand the ethical and security considerations related to LLMs, including issues of bias, fairness, responsible AI practices, data privacy risks
- Learn misinformation, deepfakes, and regulatory compliance. You will analyze real-world ethical dilemmas and explore strategies for building more trustworthy AI
- You will explore the future of LLMs by studying advances in multimodal models like GPT-4 Vision, emerging trends in model efficiency, including sparse models
- Learn memory-efficient architectures, and discover how LLMs are being applied in cross-disciplinary domains like healthcare, education, and scientific research

Requirements
- You should have an interest in AI, Natural Language Processing (NLP), and understanding how modern language models generate text, code, and other media
- A desire to learn deep learning, neural networks, and Transformer architectures.
- Interest in exploring popular LLM tools and APIs for real-world applications across industries.
- Willingness to learn how to build, fine-tune, and deploy LLMs using Python, open-source libraries, and cloud-based AI platforms.
- Familiarity with basic AI/ML concepts and Python programming is recommended.

Description
Description

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 applyLarge 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 usingHugging 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 thelatest innovations and emerging trendsin 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 yourunderstanding 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.

Who this course is for:
- AI enthusiasts who want to develop expertise in Large Language Models, deep learning, and neural networks.
- Developers, data scientists, and engineers who aim to build, fine-tune, and deploy LLM-based applications.
- Content creators, marketers, and analysts who seek to leverage LLM-powered tools for automation and content generation.
- Business leaders, product managers, and decision-makers interested in understanding how LLMs can transform industries and workflows.
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