Abstract:
At the most basic level, a chatbot is a computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person. Chatbots can be as simple as rudimentary programs that answer a simple query with a single-line response, or as sophisticated as digital assistants that learn and evolve to deliver increasing levels of personalization as they gather and process information.
You’ve probably interacted with a chatbot whether you know it or not. For example, you’re at your computer researching a product, and a window pops up on your screen asking if you need help. Or perhaps you’re on your way to a concert and you use your smartphone to request a ride via chat. Or you might have used voice commands to order a coffee from your neighborhood café and received a response telling you when your order will be ready and what it will cost. These are all examples of scenarios in which you could be encountering a chatbot.
Applications:
Driven by AI, automated rules, natural-language processing (NLP), and machine learning (ML), chatbots process data to deliver responses to requests of all kinds. There are two main types of chatbots.
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Task-oriented (declarative) chatbots are single-purpose programs that focus on performing one function. Using rules, NLP, and very little ML, they generate automated but conversational responses to user inquiries. Interactions with these chatbots are highly specific and structured and are most applicable to support and service functions—think robust, interactive FAQs. Task-oriented chatbots can handle common questions, such as queries about hours of business or simple transactions that don’t involve a variety of variables. Though they do use NLP so end users can experience them in a conversational way, their capabilities are fairly basic. These are currently the most commonly used chatbots.
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Data-driven and predictive (conversational) chatbots are often referred to as virtual assistants or digital assistants, and they are much more sophisticated, interactive, and personalized than task-oriented chatbots. These chatbots are contextually aware and leverage natural-language understanding (NLU), NLP, and ML to learn as they go. They apply predictive intelligence and analytics to enable personalization based on user profiles and past user behavior. Digital assistants can learn a user’s preferences over time, provide recommendations, and even anticipate needs. In addition to monitoring data and intent, they can initiate conversations. Apple’s Siri and Amazon’s Alexa are examples of consumer-oriented, data-driven, predictive chatbots.
This repository consists of various machine learning projects, and all of the projects must follow a certain template. I wish the contributors will take care of this while contributing in this repository.
Dataset - This folder stores the dataset used in this project. If the Dataset is not being able to uploaded in this folder due to the large size, then put a README.md file inside the Dataset folder and put the link of the collected dataset in it. That'll work!
Images - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.
Model - This folder would have your project file (that is .ipynb file) be it analysis or prediction.
https://youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi&utm_source=EKLEiJECCKjOmKnC5IiRIQ https://youtube.com/playlist?list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&utm_source=EKLEiJECCKjOmKnC5IiRIQ
- Fork the repository
- Clone your forked repository using terminal or gitbash.
- Make changes to the cloned repository
- Add, Commit and Push
- Then in Github, in your cloned repository find the option to make a pull request
- Take a look at the Existing Issues of your project and find one that interests you or create your own Issues!
- Tag the repository maintainers or issue creators to assign that issue to you.
- Wait for the Issue to be assigned to you after which you can start working on it.
- Fork the Repo and create a Branch for any Issue that you are working upon.
- Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
- Once your PR is approved, you changes will be merged into the project.
- Add Screenshots to help us know what this Script is all about.
- Repository specific contribution information is in the respective READMEs of each repo.
- Do not abuse and/or use bad language. Ensure you don't insult anyone. Be respectful and inclusive.
- Please mention your full name on your GitHub handle to be eligible for prizes.
You can take up any of the existing issues or create a new to to contribute any of your own projects!
Contribution period ends: 22 December 2022
You can refer to the following resources on Git and Github to get started and contact our Project Mentors via Discord if you have any doubts.
- Learn how to contribute to GDSC IGDTUW Autumn of Code Projects
- Go through this repository to how to contribute learn step-by-step
- Watch this video to get started
- Forking a Repo
- Cloning a Repo
- How to create a Pull Request
- Getting started with Git and GitHub
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Top 3 contributors 🍁
Special prize | Swag Kits | Shoutouts on Social Media handles | Certificate of appreciation -
Top 5 female contributors 🍁
Special prize | Swag Kits | Shoutouts on Social Media handles | Certificate of appreciation -
Top 10 contributors 🍁
Shoutouts on Social Media handles | Swag kits and lots of exciting goodies | Certificate of appreciation -
Top 25 contributors 🍁
Swag kits and lots of exciting goodies | Certificate of appreciation -
All the contributors will get a certificate of appreciation for their first successful pull request
Join our Discord to stay in touch with project mentors and for any furthur questions.
Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀