Large Language Models Professional Certificate

The Large Language Models (LLM) Professional Certificate is designed to equip professionals with the skills needed to understand, implement, and optimize applications powered by large-scale language models like GPT, BERT, and other transformer-based architectures. This certificate program dives into the inner workings of LLMs, their practical applications, and the strategies to deploy them for various use cases across industries.

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Course Objectives:

• Gain a deep understanding of how large language models (LLMs) work
• Develop and fine-tune LLMs for specific business or research use cases
• Learn prompt engineering and optimization for model performance
• Understand how to deploy and integrate LLMs into real-world applications
• Explore ethical considerations and responsible AI practices when working with LLMs

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Course Content

Module 1: Introduction to Large Language Models 

History and Evolution of LLMs:

Overview of NLP techniques pre-LLMs
Evolution from traditional NLP to transformer architectures
Milestones in LLM development (e.g., GPT, BERT, T5)

How LLMs Work:Understanding the transformer architecture
Self-attention mechanism and multi-head attention
Training on large-scale datasets: Transfer learning and fine-tuning

Hands-on Lab:
Experiment with a pre-trained transformer model using Hugging Face

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Module 2: Working with Pre-trained Language Models 

Popular Pre-trained Models:
Overview of GPT, BERT, T5, and others

How pre-trained models are used in various applications (e.g., summarization, translation, chatbots)

Loading and Using Pre-trained Models:

Working with Hugging Face’s Transformers library
Understanding tokenizers and model input/output formats
Practical examples of text generation and classification

Case Study:
Using GPT for content generation and BERT for text classification

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Why Fine-tune LLMs?
Overview of domain-specific fine-tuning
Datasets required for fine-tuning

Fine-tuning Techniques:

Customizing pre-trained models for specific tasks
Layer freezing and unfreezing strategies
Hyperparameter tuning during fine-tuning

Hands-on Lab:
Fine-tuning BERT for sentiment analysis on a custom dataset

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Module 4: Prompt Engineering and Optimization 

Understanding Prompts and Their Role:

What is prompt engineering?
Creating effective prompts for LLMs to optimize outputs

Dynamic prompting, few-shot, and zero-shot learning
Iterative prompt development and optimization

Hands-on Lab:
Using OpenAI’s GPT API with optimized prompts for various tasks (e.g., Q&A, text completion)

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Module 5: Building End-to-End Applications with LLMs 

LLM Use Cases Across Industries:
Chatbots, virtual assistants, content creation, code generation, and healthcare applications

Integration of LLMs into Applications:Using APIs to connect LLMs with other tools
Building scalable applications using LLMs in backend systems

Hands-on Lab:
Developing a chatbot using GPT-3 and integrating it into a web application

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Options for deploying LLM-based applications (on-premises, cloud-based solutions)
Best practices for integrating LLMs into production environments

Managing large-scale workloads
Using caching, model compression, and distributed inference for scalability

Hands-on Lab:
Deploying an LLM application on AWS or Azure using containers

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Module 7: Advanced LLM Techniques

Chain-of-Thought Reasoning:

Improving the reasoning ability of LLMs using step-by-step breakdowns
Implementing few-shot reasoning and task-based instructions

LLM Fine-tuning for Specific Domains: Fine-tuning techniques for medical, legal, and finance domains
Managing ethical challenges in domain-specific models

Hands-on Lab:
Fine-tuning a language model for a specialized industry use case

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Module 8: Ethical Considerations and Responsible AI 

Understanding Bias in LLMs:

Sources of bias in training data and models
Methods for mitigating bias and ensuring fairness

Ethical Use of LLMs:Responsible AI practices in development and deployment
Transparency, explainability, and data privacy concerns

Case Study:
Identifying and mitigating bias in LLM-generated outputs

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Prerequisites

Proficiency in programming languages like Python
Basic understanding of machine learning concepts

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