In this module, we will delve into the foundational aspects of OpenAI, exploring its history and powerful language models like GPT-3 and Codex. Additionally, we will cover the practical steps to set up and utilize OpenAI's API for various applications.

In this module, we will demonstrate a practical implementation of LangChain to improve question-answering capabilities and guide you through the installation of Anaconda, which is essential for our upcoming projects using Jupyter Notebooks.

In this module, we will provide an overview of the different modules within LangChain, including models, prompts, memory, indexes, chains, agents, and callbacks, highlighting their functionalities and practical applications.

In this module, we will walk you through the creation of a simple question-answering app using LangChain. You will explore different models, set up the project environment, and build an application that utilizes LangChain for effective AI-driven interactions.

In this module, we will guide you through the development of a simple conversational app using LangChain. You will learn to configure the environment, set up the chatbot, and implement custom conversational contexts for an interactive user experience.

In this module, we will explore word embeddings and their applications in machine learning projects. You will learn to implement text embeddings using LangChain and build an application that helps children understand similar words and objects.

In this module, we will delve into the prompts module in LangChain, learning how to design effective prompts, use prompt templates, and explore features like example selectors and output parsers to optimize the model's responses.

In this module, we will build a marketing campaign app using LangChain. You will learn to convert Jupyter Notebooks to Python scripts, develop a user interface with Streamlit, and integrate frontend and backend components for a seamless application.

In this module, we will focus on the memory modules in LangChain, highlighting the importance of conversation buffer memory and exploring different types of memory to enhance AI interactions by retaining and recalling past conversations.

In this module, we will guide you through the creation of a ChatGPT clone with summarization capabilities. You will learn to set up the project environment, implement a conversational view, and enhance the chatbot with features like dynamic inputs and user API keys.

In this module, we will delve into the Data Connection module in LangChain, exploring its importance and functionality. You will learn to process data, create vector embeddings, and use retrievers to fetch relevant information efficiently.

In this module, we will build an MCQ creator app using LangChain. You will learn to load documents, create embeddings, retrieve answers, and format the output in an MCQ format for practical applications.

In this module, we will explore the concept of chains within LangChain, learning how to connect different LLMs, implement generic and sequential chains, and utilize utility chains for simplifying complex tasks and saving development time.

In this module, we will explore the concepts of action agents and plan and execute agents within LangChain. You will learn how these agents make decisions and create plans, combining their functionalities for smarter and more efficient outcomes.

In this module, we will create a data analysis tool that processes CSV files based on user queries. You will learn to set up the application, implement the UI, and use LangChain and OpenAI to analyze and generate responses from CSV data.

In this module, we will develop a YouTube script-writing tool using LangChain. You will learn to set up the project environment, create the UI, and integrate LangChain to generate dynamic and engaging video scripts based on user inputs.

In this module, we will guide you through the creation of a support chatbot for websites using LangChain. You will learn to implement data scraping, set up a user interface, and integrate frontend and backend components to provide natural language assistance on your website.

In this module, we will build an automatic ticket classification tool using LangChain. You will learn to implement the UI, handle document uploads, and train a classification model to categorize tickets into different departments based on user queries.

In this module, we will create a resume screening assistance tool for HR using LangChain. You will learn to set up the project, analyze resumes, and match them with job descriptions, simplifying the screening process and enhancing HR efficiency.

In this module, we will introduce you to the LLAMA 2 model, a powerful open-source tool offered by Meta. You will learn about its versions, parameter sizes, and potential applications in generative AI, along with the steps to download and set up LLAMA 2 for local use.

In this module, we will guide you through the development of an email generator using LangChain and LLAMA 2. You will learn to set up the project, design the UI, and implement the LLAMA 2 model to generate dynamic and personalized email content.

In this module, we will build an invoice extraction bot using LangChain and LLAMA 2. You will learn to implement data extraction from PDF invoices, process the extracted data, and create a user interface for viewing and downloading the results.

In this module, we will create a text-to-SQL query helper tool using LangChain, Google Colab, and LLAMA 2. You will learn to set up the environment, implement pipelines for text prediction, and utilize LangChain to generate SQL queries based on user inputs.

In this module, we will build a customer care call summary alert tool using LangChain, OpenAI, and Zapier. You will learn to implement the UI and backend logic for processing call data, automate the summarization, and set up an email alert system for efficient customer care management.