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Large Language Models (LLMs) Fundamentals in Bengaluru, India

City details

Bengaluru combines a strong technology economy with a multilingual culture, renowned parks, and a large professional learning community.

Course cost in Bengaluru

₹35,000 INR / learner

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City-page course benefits

City-specific course benefits have not yet been provided.

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 Bengaluru, India.
  • 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 Bengaluru, India.
  • 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 Bengaluru, India.
  • 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 Bengaluru, India.
  • 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 Bengaluru, India.

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 Bengaluru, India.
  • 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 Bengaluru, India.