Welcome to the course on Generative AI with Long Language Models (LLM) using Python! This comprehensive syllabus covers various topics, from understanding LLM and its applications in different industries, to building question-answering systems, exploring embeddings, utilizing LLM for web scraping, and designing effective prompts. Gain hands-on experience with Python, major text libraries, and tools like LangChain, HuggingFace, and LlamaIndex. Whether you're a business professional or a beginner in IT or NLP, this course will empower you to leverage LLM for a wide range of business and text processing tasks. Get ready to embark on an exciting journey into the world of LLM and Generative AI!

Module 1: Introduction to Large Language Models

• Introduction to Generative AI

  • Overview of Generative AI and its real-world applications
  • Introduction to the concept of Long Language Models (LLM)
  • Demonstration of LLM usage in various business contexts
  • Setting up the development environment
  • Introduction to Python as the main programming language in the training

• Introduction to Python for Language Preprocessing

  • Python basics for beginners
  • Variables, data types, basic operations, and functions in Python
  • Control structures: Conditional statements and loops

• Basics of Language Processing

  • Introduction to Natural Language Processing (NLP)
  • Exploring word embeddings and their role in language models
  • Introduction to major text libraries in Python (e.g., NLTK, spaCy)
  • Understanding text preprocessing and tokenization
  • Demonstration of library usage for simple text tasks

Module 2: Creating Conservational AI with Large Language Models for Business

• Large Language Models: Architecture, Transformer, and Key Concepts

  • Overview of Large Language Models and their Architecture
  • Understanding what is Transformer
  • Explanation of pre-training and fine-tuning of language models
  • Introduction to popular Large Language Models like GPT-3, GPT-2, and BERT
  • Understanding the capabilities and limitations of Large Language Models
  • Explanation of the LangChain concept
  • Setting the API key and .env

• Building Question-Answering Systems with Large Language Models

  • Introduction to Question-Answering System
  • Steps involved in connecting databases with LLM
  • Basics of building a Question-Answering System using LLM with a database
  • Demonstration of using OpenAI and LangChain to build a Question-Answering System
  • Using LangChain and OpenAI to build a Question-Answering System with text data
  • Steps involved in connecting CSV data with LLM
  • Demonstration of using LangChain and OpenAI to build a Question-Answering System with text data

• Text Generation with HuggingFace

  • Introduction to the Text Generation model in HuggingFace
  • Setting the .env token key
  • Applying HuggingFace's Inference API to use LLM without OpenAI credits
  • Integrating HuggingFace's Inference API into the previously built Question-Answering System
  • Demonstration of using HuggingFace's Inference API to build a Question-Answering System

Module 3: Understanding Embeddings in LLM

• Understanding Embeddings in Large Language Models (LLM) for Natural Language Processing

  • Basic concepts of embeddings in LLM
  • Usage of embeddings in natural language processing
  • Demonstration of embeddings usage in text analysis

• Advanced Embeddings in Large Language Models (LLM) for Text Processing

  • In-depth understanding of embeddings in LLM
  • Introduction to popular embedding techniques like Word2Vec and FastText
  • Implementation of embeddings in text processing using Python
  • Demonstration of embedding techniques in text processing tasks

• Advanced Applications of Embeddings in Text Processing with Large Language Models (LLM)

  • Application of embeddings in advanced text processing
  • Usage of embeddings for text classification and contextual understanding
  • Demonstration of embeddings usage in advanced tasks

Module 4: Build Chat AI apps with Streamlit + LangChain

• Build Chat AI apps with Streamlit + LangChain

  • Introduction to Streamlit and LangChain
  • Building the Chat Interface
  • Integrating NLP with LangChain
  • Building a Simple Chatbot
  • Adding Advanced Features
  • Deployment and Sharing

• Optimizing Large Language Models (LLM) for Performance Enhancement

  • Demonstration of using LlamaIndex
  • Designing effective prompts for LLM
  • Using LangChain's Caching to enhance LLM performance
  • Demonstration of prompt design and caching to maximize LLM usage

• Leveraging Large Language Models (LLM) for Web Scraping and Information Extraction

  • Using LLM for query any website
  • Information Extraction set up using LLM
  • Introduction to LlamaIndex and its usage in information extraction
  • Demonstration of using LangChain and OpenAI to build a Question-Answering System with website data

• Tips and Tricks in prompting with Large Language Models (LLM)

  • A language for LLM prompt design: Guidance
  • Understanding the ethical considerations of Generative AI
  • Impact on privacy, bias, and misinformation
  • Responsible user of Large Language Models in society
  • Discussion on the future of Generative AI and its potential impact