Master Generative AI in Just 4 Weeks

Learn the fundamentals of Generative AI and the LangChain framework, setting the foundation for advanced AI development.

Master Generative AI: A Step-by-Step 4-Week Plan

🎯 Learning Objectives

• Understand modern Large Language Models (LLMs) and their capabilities
• Learn how LangChain works, including its core components and architecture
• Master prompt engineering techniques for optimal AI interactions

📚 Key Topics Covered

• Introduction to the GenAI landscape
• Pre-trained LLMs: GPT, BERT, and RoBERTa models
• LangChain concepts: chains, agents, memory systems, and prompts
• Prompt engineering strategies: zero-shot and few-shot examples

🛠️ Practical Activities

• Set up a complete development environment for GenAI projects
• Build and run your first LangChain application using OpenAI or GPT-4
• Create a functional conversational chatbot from scratch

Master document processing and semantic search capabilities, teaching you to build intelligent information retrieval systems.

🎯 Learning Objectives

• Master techniques for structuring and storing text data for efficient retrieval
• Build sophisticated question-answering systems using vector search technology

📚 Core Technologies

• Chunking techniques using recursive text splitting for optimal processing
• Sentence embeddings using OpenAI and Hugging Face models
• Vector databases: Implementation with FAISS, Chroma, and Weaviate
• Retrieval Augmented Generation (RAG) architecture systems

🛠️ Hands-On Projects

• Create vector stores using real PDF documents and website data
• Build complete RAG pipelines using LangChain + Chroma integration

Learn advanced fine-tuning techniques to customize large language models for specific use cases and domains.

🎯 Learning Objectives

• Master the fundamentals of LLM fine-tuning for custom applications
• Explore parameter-efficient tuning methods like LoRA (Low-Rank Adaptation)

📚 Advanced Concepts

• Overview of fine-tuning: classification, summarization, and custom task optimization
• LoRA vs full fine-tuning approaches and their trade-offs
• Model evaluation metrics: Perplexity, ROUGE, BLEU, and custom metrics
• Data collection, labeling, and preprocessing for optimal results

🛠️ Practical Implementation

• Fine-tune BERT or DistilBERT using Hugging Face Transformers
• Train models on IMDB dataset or your own custom datasets
• Implement comprehensive model evaluation using industry-standard metrics

Focus on deploying GenAI applications in production environments and working with advanced AI agents for real-world solutions.

🎯 Learning Objectives

• Deploy GenAI applications in production environments
• Explore advanced LangChain Agents and their capabilities
• Complete a comprehensive capstone project showcasing your skills

📚 Production-Ready Skills

• LangChain Agents: decision-making systems and intelligent tool usage
• Error handling: robust error management and response post-processing
• Deployment platforms: using Streamlit and Gradio for user interfaces
• Future trends: agents, memory systems, and multimodal applications

🛠️ Advanced Projects

• Build sophisticated LangChain Agents that use multiple tools for complex query answering
• Deploy complete GenAI applications with user-friendly interfaces

Choose from four industry-relevant capstone projects designed to showcase your GenAI expertise and build portfolio-worthy applications.

Top Applications of AI-Powered Content Generation

💼 Available Project Options

• GenAI-powered Resume Critique Bot: Build an intelligent AI assistant that analyzes resumes and provides detailed, actionable feedback for job seekers
• PDF-based Legal/Medical Q&A App: Create a specialized question-answering system for professional documents in legal or medical domains
• Product Feature Comparator Chatbot: Develop an Amazon-style e-commerce AI assistant that helps users compare product features and make informed decisions
• Domain-Specific Assistant: Design a specialized AI assistant for Healthcare, Education, or HR that understands industry-specific terminology and workflows

Our comprehensive assessment system ensures you master each concept while building a strong portfolio of GenAI projects.

📊 Assessment Breakdown

• Module assignments (40%): hands-on coding projects for each of the 4 modules
• Mid-course project (30%): comprehensive project combining Modules 1-2 concepts
• Final capstone project (30%): complete GenAI application demonstrating all skills

✅ Certification Requirements

• Complete all module assignments with functional, well-documented code
• Submit detailed project documentation and technical reports
• Present and defend your capstone project to industry experts
• Achieve minimum 70% overall score across all assessments
• Participate actively in peer code reviews and technical discussions

Comprehensive career preparation and industry recognition upon successful course completion in the rapidly growing GenAI field.

Job Roles:

• GenAI Engineer
• LLM Specialist
• AI Product Manager
• Prompt Engineer
• RAG Systems Developer
• Conversational AI Developer
• AI/ML Consultant
• Machine Learning Engineer
• AI Research Scientist
• AI Solutions Architect

Industries:

• Technology and Software
• Finance and Banking
• Healthcare and Pharmaceuticals
• E-commerce and Retail
• Media and Content Creation
• Education and EdTech
• Legal and Professional Services
• Customer Service and Support
• Research and Development
• Consulting and Advisory

Career Support:

• Resume review and GenAI portfolio development
• You’ll learn to generate images using Generative AI tools.
• Technical interview preparation for AI roles
• Mock interviews with industry experts
• LinkedIn profile optimization for GenAI positions
• Job placement assistance and AI industry networking
• Continuous learning resources and GenAI updates
• Alumni network and mentorship programs
• Industry certification guidance for AI specializations