Generative AI Course

Course Overview

Generative AI courses typically cover the fundamentals of AI and machine learning, then delve into specific generative models like GANs and VAEs. They also explore how to use tools like Python and deep learning frameworks like PyTorch and TensorFlow to build and deploy Gen AI models.

Course Modules

Module 1. Introduction
Overview of AI evolution, Gen AI, and course roadmap.

Module 2. Python Control Flow
Covers if-else, loops, and logical conditions in Python.

Module 3. Data Structures Using Python
Learn lists, dictionaries, tuples, and sets with examples.

Module 4. Functions in Python
Understand defining and calling functions with arguments.

Module 5. Modules and Packages
Create and import custom modules and Python packages.

Module 6. File Handling
Read, write, and manage files in Python effectively.

Module 7. Exception Handling
Handle errors using try, except, finally blocks.

Module 8. OOPs: Classes and Objects
Understand object-oriented programming with real-world examples.

Module 9. Streamlit with Python
Build simple web apps for ML models using Streamlit.

Module 10. ML for NLP
Apply machine learning techniques to natural language data.

Module 11. Deep Learning for NLP
Use neural networks for NLP tasks like classification.

Module 12. Simple RNN Intuition
Learn how Recurrent Neural Networks work for sequences.

Module 13. ANN Project Implementation
Build and deploy an artificial neural network project.

Module 14. End-to-End DL with Simple RNN
Full RNN-based project from preprocessing to prediction.

Module 15. LSTM Intuition
Understand Long Short-Term Memory networks in depth.

Module 16. LSTM & GRU Project
Use LSTM and GRU to build a next-word predictor.

Module 17. Bidirectional RNN
Explore bidirectional RNNs for context-rich NLP models.

Module 18. Encoder-Decoder Architecture
Learn sequence-to-sequence models using encoder-decoder setup.

Module 19. Attention Mechanism
Apply attention layers in seq2seq for better accuracy.

Module 20. Transformers
Dive into Transformer architecture powering modern LLMs.

Module 21. Intro to Generative AI & LLMs
Understand what LLMs are and how Gen AI works.

Module 22. LangChain Introduction
Start working with LangChain for LLM orchestration.

Module 23. LangChain + OpenAI Setup
Connect LangChain with OpenAI for building LLM apps.

Module 24. LangChain Core Components
Deep dive into chains, tools, prompts, and memory.

Module 25. OpenAI and Ollama Setup
Integrate local and cloud LLMs into LangChain workflows.

Module 26. LLM App using LCEL
Use LangChain Expression Language to build quick apps.

Module 27. Chatbot with History
Build contextual chatbots that remember conversation flow.

Module 28. RAG Doc Q&A Chatbot
Build Retrieval-Augmented Generation (RAG) bot with memory.

Module 29. PDF Conversational Chatbot
Talk with PDF content using LangChain tools.

Module 30. LangChain Search Engine
Create a custom search engine with agents and tools.

Module 31. SQL DB Chatbot Project
Query SQL databases via LLM using LangChain SQL tools.

Module 32. Text Summarization with LangChain
Summarize text using chains and OpenAI models.

Module 33. Gen AI Project: YouTube & URLs
Summarize content from YouTube and web URLs.

Module 34. Hugging Face + LangChain
Integrate Hugging Face models into LangChain workflows.

Module 35. PDF Query with AstraDB
Ask questions to PDFs using RAG and AstraDB.

Module 36. Code Assistant with CodeLlama
Use CodeLlama to build a multi-language coding assistant.

Module 37. Gen AI Deployment
Deploy Gen AI apps using Streamlit and Hugging Face.

Module 38. Gen AI with AWS
Use AWS services for Gen AI app deployment.

Module 39. NVIDIA NIM + LangChain
Get started with NVIDIA NIM models in LangChain.

Module 40. CrewAI Stock Agent System
Create stock analysis app with multi-agent system.

Module 41. Graph DB & Cypher Intro
Learn graph databases and Cypher query basics.

Module 42. LangChain + Graph DB
Implement graph-based retrieval in LangChain apps.

Module 43. Intro to LLM Fine-Tuning
Understand what fine-tuning LLMs involves.

Module 44. End-to-End LLM Fine-Tuning
Train and fine-tune your own LLM step-by-step.

Module 45. LangGraph for Multi-Agent Apps
Build stateful, multi-actor Gen AI workflows using LangGraph.

Module 46. MCP & Agent2Agent Protocol
Learn protocols for enabling multi-agent communication.