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Large Language Models (LLMs) Fundamentals in Sharjah, United Arab Emirates

City details

Sharjah brings together regional culture, modern infrastructure and expanding professional opportunities.

City speciality: Modern infrastructure and regional culture

Course cost in Sharjah

$799 USD / learner

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Course curriculum

Module 1: Introduction to Large Language Models
- Understanding Large Language Models and their role in Generative AI.
- Evolution of Natural Language Processing and language models.
- Exploring capabilities, limitations, and common LLM use cases.
- Understanding the LLM ecosystem and applications in Sharjah, United Arab Emirates.
Module 2: Natural Language Processing Fundamentals
- Introduction to text processing and language representation.
- Understanding vocabulary, tokens, tokenization, and text encoding.
- Exploring semantic relationships and language representations.
- Understanding how NLP supports modern LLM applications.
Module 3: Transformer Architecture
- Introduction to the Transformer architecture.
- Understanding attention and self-attention mechanisms.
- Exploring encoder and decoder components.
- Understanding positional encoding and its role in language processing.
Module 4: Tokens, Embeddings, and Context
- Understanding tokenization and token management.
- Exploring word, sentence, and contextual embeddings.
- Understanding context windows and sequence processing.
- Examining how context influences LLM responses in Sharjah, United Arab Emirates.
Module 5: LLM Training and Pre-Training
- Understanding datasets and data preparation for LLM training.
- Exploring pre-training and language modeling objectives.
- Understanding computational requirements and training infrastructure.
- Exploring challenges associated with large-scale model training.
Module 6: Fine-Tuning and Instruction Tuning
- Understanding the purpose of model fine-tuning.
- Exploring supervised fine-tuning and instruction tuning.
- Understanding parameter-efficient fine-tuning concepts.
- Identifying scenarios where customization can support specific AI applications.
Module 7: LLM Inference and Generation
- Understanding the LLM inference process.
- Exploring text generation and decoding strategies.
- Understanding temperature, top-k, and top-p sampling.
- Examining factors that influence model responses and performance in Sharjah, United Arab Emirates.
Module 8: LLM Applications and Use Cases
- Exploring conversational AI and virtual assistants.
- Understanding text generation, summarization, and classification.
- Exploring question answering and information extraction.
- Examining LLM applications across business and technology environments.
Module 9: LLM Evaluation and Responsible AI
- Understanding approaches for evaluating LLM quality and performance.
- Exploring accuracy, relevance, consistency, and response quality.
- Understanding hallucinations, bias, privacy, and security considerations.
- Applying responsible AI principles to LLM applications in Sharjah, United Arab Emirates.
Module 10: Practical LLM Applications and Future Trends
- Exploring practical workflows using modern LLM technologies.
- Understanding LLM-powered application development at a foundational level.
- Introduction to RAG, AI agents, and LLM application architectures.
- Exploring emerging LLM trends and pathways for advanced AI learning in Sharjah, United Arab Emirates.

Exam details

Exam Level: Beginner–Intermediate – Large Language Models Fundamentals
Exam Type: Online computer-based examination consisting of multiple-choice questions and practical LLM-based tasks in Sharjah, United Arab Emirates.
Number of Questions: 40 questions
Exam Duration: 90 minutes
Passing Score: 70% (28 out of 40)
Question Format: Multiple-choice questions covering LLM fundamentals, NLP, transformers, tokens, embeddings, attention mechanisms, training, fine-tuning, inference, and evaluation.
Practical Assessment: Candidates may complete practical tasks involving LLM interaction, text generation, model parameters, prompt-based activities, and analysis of LLM outputs.
Key Exam Topics: LLM architecture, transformer models, tokenization, embeddings, context windows, pre-training, fine-tuning, inference, decoding strategies, LLM applications, evaluation, and responsible AI.
Prerequisites: Basic knowledge of Artificial Intelligence and Generative AI is recommended. Basic Python knowledge is beneficial but not mandatory.
Assessment Platform: Online assessment through the StepMerit platform.
Certification: Successful candidates receive a StepMerit Course Completion Certificate.
Exam Preparation: Hands-on exercises, demonstrations, and practical LLM activities help learners prepare for the assessment in Sharjah, United Arab Emirates.