Section outline

• Select section Day 1: Introduction to Neural Networks & AI Models

  Day 1: Introduction to Neural Networks & AI Models

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  Objective: Understand the foundation of AI, ML, and neural networks

  Introduction to Machine Learning, Deep Learning, and Generative AI
  Understanding Neurons, Gradient Descent, Backpropagation (optional)
  Training models using Linear & Logistic Regression
  Lab: Creating models using Linear & Logistic Regression
  
• Select section Day 2: Generative AI & ChatGPT

  Day 2: Generative AI & ChatGPT

  Objective: Learn how LLMs work and apply ChatGPT for real-world tasks

  What is Generative AI?
  Understanding Large Language Models (LLMs)
  Using ChatGPT for various applications
  Lab: Creating a chatbot using OpenAI API & Streamlit/Gradio
• Select section Day 3: Prompt Engineering

  Day 3: Prompt Engineering

  Objective: Master different prompting techniques for AI optimization

  Best practices for structuring prompts
  Techniques: Zero-shot, Few-shot, Chain-of-thought, Tree-of-thought, ReAct prompting
  Lab: Experimenting with prompts using OpenAI Playground
• Select section Day 4: LangChain – AI Application Development

  Day 4: LangChain – AI Application Development

  Objective: Build and optimize AI workflows with LangChain

  Understanding AI-enabled applications
  Creating chat-based AI workflows
  Implementing memory in AI applications
  Lab: Build a chatbot using LangChain & Streamlit
• Select section Day 5: Vector Stores & Retrieval-Augmented Generation (RAG)

  Day 5: Vector Stores & Retrieval-Augmented Generation (RAG)

  Objective: Learn how to enhance AI models with external data sources

  Introduction to Embeddings & Vector Stores
  Working with Chroma DB & Pinecone
  Lab: Enhance Financial Advisor App with RAG
• Select section Day 6: AI Tools & Agents

  Day 6: AI Tools & Agents

  Objective: Configure and utilize AI agents for automation

  Configuring ChatGPT tools & API functions
  Developing database search & web search tools
  Lab: Creating a custom database tool for AI applications
• Select section Day 7: LangServe – AI Deployment

  Day 7: LangServe – AI Deployment

  Objective: Deploy AI-powered solutions as APIs

  Introduction to LangServe
  Deploying AI applications as APIs
  Lab: Exposing Runnables as API endpoints
• Select section Day 8: LangGraph – Advanced AI Workflows

  Day 8: LangGraph – Advanced AI Workflows

  Objective: Build multi-agent AI applications with LangGraph

  Understanding LangGraph components
  Implementing Multi-Agent AI Applications
  Lab: Creating a Customer Support AI using LangGraph
• Select section Day 9: LlamaIndex – AI Data Indexing

  Day 9: LlamaIndex – AI Data Indexing

  Objective: Use LlamaIndex for AI-powered data retrieval

  Building knowledge-based AI applications
  Using SQLTableRetrieverQueryEngine for AI-driven databases
  Lab: Creating a chatbot that converts Natural Language to SQL Queries
• Select section Day 10: AutoGen, Crew AI & Azure OpenAI

  Day 10: AutoGen, Crew AI & Azure OpenAI

  Objective: Automate workflows and deploy AI applications in production

  Understanding AutoGen for AI automation
  Multi-Agent AI with Crew AI
  Deploying models using Azure OpenAI
  Lab: Creating an AI-powered chatbot similar to ChatGPT