Large Language Models Professional Certificate In Riyadh, Jeddah, Saudi Arabia, Dubai, UAE

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.

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

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

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

Module 3: Fine-tuning Large Language Models

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

Module 4: Prompt Engineering and Optimization

Understanding Prompts and Their Role:

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

Advanced Prompting Techniques:

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)

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

Module 6: Deploying and Scaling LLM Applications

Deployment Strategies:

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

Scaling and Performance Optimization:

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

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

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