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Master AI with this hands-on video and build your very own automated question generator!
Perfect for beginners and experienced coders alike, this comprehensive guide walks you through creating an intelligent application that generates interview or exam questions with answers. Using Python, LangChain, GPT-3.5, and FAISS, you'll develop a powerful tool to automate tedious tasks and enhance productivity.
What you'll learn in this video:
- How to integrate Python with LangChain and GPT-3.5 for AI-driven question generation.
- Setting up a vector database (FAISS) for efficient data handling.
- Building a user-friendly interface with FastAPI for seamless interactions.
- Modular coding practices to create scalable and reusable components.
- Practical insights into embedding models and prompt engineering.
Why this video is perfect for you:
- Comprehensive walkthrough suitable for beginners and advanced learners.
- Practical examples to help you grasp complex concepts effortlessly.
- Ideal for teachers, recruiters, or developers looking to streamline question creation processes.
Kickstart your programming journey or level up your coding skills today! Hit play and code along as we bring this exciting project to life. Don’t forget to Like, Subscribe, and hit the notification bell to stay updated on future tutorials from Euron. Let’s build something amazing together!
#datascienceproject #datascienceprojects #machinelearningprojects #mlprojects #machinelearningprojectswithsourcecode
CHAPTERS:
00:00 - Introduction
00:48 - Project Idea
04:33 - Project High Level Architecture
09:53 - Project Implementation
11:05 - Writing a Good README File
14:02 - Setting Up the Project
16:52 - Loading Data from PDF
20:59 - Chunking Operation
22:56 - Token Text Splitter
28:23 - Generating Questions from Chunks
29:26 - Advanced Application: Sustainable Development Goals
30:12 - Creating Prompt Template
31:35 - Refined Prompt Template
34:03 - Generating Questions
36:20 - Creating Embedding Model
40:53 - Source Folder Structure
44:15 - Creating Helper.py
47:28 - LLM Pipeline Creation
48:28 - FastAPI Endpoint Setup
52:43 - Running FastAPI Endpoint
54:04 - Running the Flask App
56:14 - Final Output
56:35 - End
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