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LLM: The Brain Behind Modern AI

Name: LLM: The Brain Behind Modern AI
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Large Language Models (LLMs) are advanced AI systems trained on massive amounts of text data to understand and generate human-like language. They power applications like chatbots, content creation, coding assistants, and intelligent automation.

4.1

|3h 19m

What's Included

Lessons

3h 19m

Duration

Certificate

What You'll Master

Skills and outcomes you'll walk away with

Module 1: Foundations of Natural Language Processing

Module 2: The Transformer Architecture and Attention

Module 3: Pre-training Objectives and Language Modeling

Practical Project - 1

Module 4: Prompt Engineering and In-Context Learning

Module 5: Fine-Tuning and Parameter-Efficient Methods (PEFT)

Course Curriculum

12 lessons • 3h 19m total

Module 1: Foundations of Natural Language Processing

16m

Module 2: The Transformer Architecture and Attention

20m

Module 3: Pre-training Objectives and Language Modeling

20m

Practical Project - 1

11m

Module 4: Prompt Engineering and In-Context Learning

18m

Module 5: Fine-Tuning and Parameter-Efficient Methods (PEFT)

18m

Module 6: Alignment and Reinforcement Learning from Human Feedback

18m

Practical Project - 2

12m

Module 7: Retrieval-Augmented Generation (RAG) and Vector DBs

18m

Module 8: LLM Agents and Tool Calling

17m

Module 9: MLOps, Optimization, and Deployment for LLMs

18m

Practical Project - 3

13m

Certification Path

Certification Exam

27 multiple-choice questions • 70% passing score required

Final Project: Designing and Evaluating a Domain-Specific LLM Application

For your final assignment, you will synthesize the concepts learned in 'LLM: The Brain Behind Modern AI' by developing a functional, domain-specific application powered by a Large Language Model. You must choose a specific use case (e.g., legal document summarization, educational tutor, or automated code reviewer). The project requires you to select an appropriate base model, implement advanced prompt engineering, and utilize either Retrieval-Augmented Generation (RAG) or parameter-efficient fine-tuning (PEFT). You will submit a GitHub repository containing your application code, a deployed prototype, and a comprehensive 5-page technical report. The report must detail your architectural decisions, data pipeline, hallucination mitigation strategies, and an ethical impact assessment.

Verified Certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹299