What will you learn in Essentials of Large Language Models: A Beginner’s Journey Course
LLM fundamentals & architecture: Understand key differences between language models and large language models, explore components, transformer architecture, evolution from GPT‑2 to modern variants.
Types, capabilities & limitations: Learn various LLM types, their strengths/weaknesses, and appropriate use cases across domains.
GPT‑2 deep dive: Study GPT‑2 as a prototypical LLM—architecture, training, functionality, and behavior.
Fine‑tuning in practice: Hands-on experience fine‑tuning LLMs on custom datasets: selection, data prep, model training, and performance evaluation.
Model comparison & evaluation: Learn methods to evaluate performance differences between LLMs and compare outputs quantitatively and qualitatively.
Program Overview
Module 1: Course Introduction & Ethics
⏳ ~15 minutes
Topics: Overview of LLM applications, ethical considerations (bias, misuse), and course roadmap.
Hands-on: Reflective prompts on bias and real-world impact of LLMs.
Module 2: LLM Basics & Architecture
⏳ ~30 minutes
Topics: Key components of LLMs, model scaling, transformer mechanics.
Hands-on: Quiz on LLM structure and interactive architecture summary.
Module 3: Exploring GPT‑2
⏳ ~30 minutes
Topics: GPT‑2’s model structure, parameter patterns, strengths and limitations.
Hands-on: Analyze GPT‑2 outputs and compare with input prompts.
Module 4: Fine‑tuning Fundamentals
⏳ ~45 minutes
Topics: Step-by-step fine‑tuning: selecting models, preparing data, training, evaluating.
Hands‑on: Fine‑tune a small LLM on sample text data via interactive environment.
Module 5: Performance Evaluation & Comparison
⏳ ~45 minutes
Topics: Metrics for evaluation (perplexity, accuracy), qualitative analysis, model benchmarking.
Hands-on: Compare two model versions and evaluate using defined metrics.
Module 6: Use Cases & Next Steps
⏳ ~30 minutes
Topics: Common LLM use cases: chatbots, summarization, classification; deployment pathways.
Hands-on: Draft a project roadmap using LLM techniques for a sample application.
Module 7: Final Quiz & Closure
⏳ ~15 minutes
Topics: Quiz covering all key learnings and next-step resource suggestions.
Hands-on: Complete final evaluation and course takeaway reflection.
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Job Outlook
Generative AI readiness: Builds essential skills for roles like LLM Engineer, ML Engineer, Data Scientist, and AI Product Specialist.
Industry relevance: Applies to NLP, content generation, summarization, and AI tooling roles across sectors.
Portfolio asset: Fine-tuning demo and model comparison project makes a solid portfolio addition for interviews.
Foundation for LLMOps: Prepares learners to explore deployment, prompt engineering, and ethical implementation workflows.
Specification: Essentials of Large Language Models: A Beginner’s Journey
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