Complete Generative AI This comprehensive course is designed to equip learners with the skills to build, deploy, and optimize generative AI applications using the LangChain framework and Huggingface’s state-of-the-art models.
Through hands-on projects, step-by-step tutorials, and real-world applications, participants will master the fundamentals of generative AI, understand advanced architectures, and develop scalable AI solutions.
Perfect for AI enthusiasts, developers, and professionals aiming to leverage generative AI in domains like chatbots, content generation, and data augmentation.
Duration: 54 hours
Level: Beginner to Advanced
Prerequisites: Basic Python programming knowledge, familiarity with software engineering concepts (e.g., git, pipenv), and a passion for AI.
Instructor: Inspired by Krish Naik and KRISHAI Technologies Private Limited Rating: 4.7 (3,181 ratings)
Course Objectives
- Understand the fundamentals of generative AI and its distinction from traditional AI models.
- Master the LangChain framework for building AI-driven applications.
- Leverage Huggingface’s pre-trained models for natural language processing (NLP) tasks.
- Develop and deploy Retrieval-Augmented Generation (RAG) pipelines.
- Gain hands-on experience through real-world projects like chatbots and content generators.
- Learn deployment strategies for cloud and on-premise environments.
- Fine-tune and customize Huggingface models for specific use cases.
Course Curriculum
Module 1: Introduction to Generative AI
Duration: 4 hours
Objective: Understand the core concepts of AI and generative AI.
Lessons:
What is Generative AI?
Definition and scope of AI, machine learning, and generative AI.
Differences between traditional AI and generative AI models.
Real-world applications: chatbots, content generation, and data augmentation.
Overview of Large Language Models (LLMs)
Introduction to LLMs (e.g., GPT, Llama, BERT).
Role of Transformers in generative AI.
Setting Up the Development Environment
Installing Python, Anaconda, and essential libraries (NumPy, Pandas, etc.).
Introduction to Jupyter notebooks and IDEs (e.g., PyCharm, VSCode).
Hands-On: First Steps with Python for AI
Writing basic Python scripts for data manipulation.
Exploring libraries like NLTK and SpaCy for NLP basics.
Project: Build a simple text generator using a pre-trained model from Huggingface.
Module 2: Mastering LangChain
Duration: 8 hours
Objective: Learn the LangChain framework and its role in AI application development.
Lessons:
Introduction to LangChain
Overview of LangChain and its ecosystem (Chains, Agents, DocumentLoader, TextSplitter).
Understanding LangChain Expression Language (LCEL).
Building Chains and Agents
Creating prompt chains for structured AI interactions.
Implementing LangChain agents for dynamic decision-making.
Memory and Context in LangChain
Adding memory to AI applications for contextual awareness.
Managing conversation history in chatbots.
Hands-On: LangChain Basics
Setting up a LangChain environment (LangChain v0.3.0).
Building a simple Q&A system using LangChain.
Project: Develop a LangChain-based chatbot that maintains conversation context.
Module 3: Huggingface and Transformers
Duration: 10 hours
Objective: Master Huggingface’s Transformers library for NLP tasks.
Lessons:
Introduction to Huggingface
Overview of Huggingface ecosystem and pre-trained models.
Using the pipeline() function for text generation, classification, and more.
Understanding Transformers
Encoder, decoder, and encoder-decoder architectures.
Use cases for Transformers in generative AI.
Fine-Tuning Huggingface Models
Techniques for fine-tuning models (e.g., Instruction Fine-tuning, PEFT).
Customizing models for specific domains (e.g., sentiment analysis, summarization).
Hands-On: Huggingface in Action
Implementing text generation with GPT-2.
Performing sentiment analysis using BERT.
Project: Fine-tune a Huggingface model for a custom text summarization task.
Module 4: Retrieval-Augmented Generation (RAG)
Duration: 8 hours
Objective: Develop RAG pipelines to enhance generative AI performance.
Lessons:
Introduction to RAG
Combining generative models with retrieval systems.
Role of vector databases (e.g., Pinecone, FAISS).
Building RAG Pipelines
Creating embeddings for text data.
Integrating vector stores with LangChain.
Optimizing RAG Systems
Improving accuracy and performance of RAG pipelines.
Handling large-scale datasets for retrieval.
Hands-On: RAG Implementation
Building a knowledge retrieval assistant using LangChain and FAISS.
Project: Create a RAG-based question-answering system for a specific domain (e.g., Wikipedia data).
Module 5: Deployment and Optimization
Duration: 10 hours
Objective: Learn to deploy and optimize generative AI models.
Lessons:
Deployment Strategies
Deploying models to cloud platforms (e.g., AWS, Azure).
Setting up on-premise servers for AI applications.
Scalability and Reliability
Ensuring high availability and low latency.
Load balancing and auto-scaling techniques.
Monitoring and Optimization
Techniques for monitoring deployed models.
Best practices for model updates and maintenance.
Hands-On: Model Deployment
Deploying a LangChain-Huggingface application to a cloud platform.
Project: Deploy a generative AI chatbot to a cloud server with monitoring capabilities.
Module 6: Real-World Projects
Duration: 12 hours
Objective: Apply learned concepts to build portfolio-ready projects.
Projects:
Chatbot for Customer Support
Build an AI-powered chatbot using LangChain and Huggingface.
Implement memory and RAG for contextual responses.
Content Generation Tool
Create a tool for generating blog posts or social media content.
Fine-tune a Huggingface model for specific content styles.
Data Augmentation System
Develop a system for generating synthetic data for machine learning tasks.
Use RAG to enhance data quality.
Text-to-Image Generator
Integrate Huggingface’s Diffusers library with LangChain for multimodal AI.
Create a tool for generating images from text prompts.
Module 7: Capstone Project
Duration: 2 hours
Objective: Synthesize all skills in a comprehensive project.
Capstone Project: Build a full-stack generative AI application (e.g., a knowledge management system) that integrates LangChain, Huggingface, RAG, and cloud deployment. The project includes:
- A front-end interface using Streamlit or Gradio.
- A back-end with FastAPI or Flask.
- A vector database for RAG.
- Monitoring and optimization features.
Course Features
- Hands-On Learning: 50+ hours of video content, coding exercises, and projects.
- Real-World Projects: Build portfolio-ready applications in chatbots, content generation, and more.
- Community Support: Access to a dedicated forum for peer and instructor interaction.
- Certificate of Completion: Earn a certificate to showcase your skills.
- Lifetime Access: Revisit course materials anytime.
Target Audience
- AI enthusiasts eager to explore generative AI.
- Developers looking to integrate AI into their applications.
- Professionals aiming to build and deploy scalable AI solutions.
- Students and researchers interested in NLP and generative models.
Learning Outcomes
By the end of this course, you will:
- Build advanced generative AI applications using LangChain and Huggingface.
- Understand and implement RAG pipelines for enhanced AI performance.
- Fine-tune and deploy Huggingface models for custom use cases.
- Develop scalable, production-ready AI applications.
- Gain hands-on experience with real-world projects in multiple domains.
Resources
- Code Repository: Access all project code on GitHub.
- Supplementary Materials: Downloadable datasets, notebooks, and templates.
- Recommended Tools: Python, Anaconda, Jupyter, LangChain (v0.3.0), Huggingface Transformers, Diffusers, Gradio, Pinecone, FAISS.
Enrollment Details
- Platform: Udemy
- Link: Complete Generative AI Course with LangChain and Huggingface
- Discount: Check for current promotions (e.g., $9 for first 1,000 enrollments or 30 days).
- Access: Lifetime access to course materials and updates.
About the Instructor
Krish Naik is the ex-co-founder and Chief AI Engineer of iNeuron, with over 15 years of experience in machine learning, deep learning, and generative AI. As an educator and mentor, Krish has trained thousands of students through Krish AI Technologies, focusing on practical, industry-relevant AI skills.
Join this exciting journey to master generative AI and build cutting-edge applications with LangChain and Huggingface!
