Build AI based Chatbot using open source framework - Online Workshop

**** Sorry! Instructor has something urgent to attend to. We have moved the hands-on workshop to next week - Sunday, April 19th 10am. Sorry about that ****

Build AI based Chatbot using open source framework
Online Workshop by Yogesh Kothiya, Data Scientist at Oorwin Labs | Conversational AI | Runs AI community https://colearninglounge.com
Advance registration required @ http://bit.ly/cs-rsvp

Webinar room: Blackboard Collaborate - https://us.bbcollab.com/guest/9946ca1d2dc24382b29a8f92a09056a4
Shortcut: https://bit.ly/utdcstalks

Conversational AI systems are becoming an indispensable part of the human ecosystem. Apple’s Siri, Amazon’s Alexa and Google’s Assistant is our go-to app. Rasa is building tools that can be used by developers with minimal experience in ML to build conversational assistants that work for everyone. Its open-source stack which is highly customizable, modular and completely language agnostic.

In this workshop, we will be building a chatbot where users can search for a restaurant using location and cuisine using this library. First, we will go through scope of the bot and all concepts of the library to make a chatbot. In this process, we will have to prepare training data to train a bot and write custom logic to fulfill the actions. Also, we will be using the BING map API to extract location from the user utterance and Zomato API to get the list of the restaurant based on the user’s criteria.

Get to know the scope of the bot - ~10 mins
Introduction to Zomato API - ~5 mins
Introduction to rasa - ~3 mins
Get to know to rasa components

1. NLU - ~10 mins
2. Core - ~5 mins
   Get to know rasa configurations - ~5 mins
   Going through training data and configuration files - ~10 mins
   Get to know rasa commands - ~5 mins
   Walkthrough of rasa x - ~7 mins
   Demo of complete bot - ~5 mins
   Hands-on - ~15 mins

This workshop helps the attendee to get started and grow in the space of conversational AI. For data analyst enthusiast it provides new opportunities to explore conversational analytics. The developed bot can be deployed to any conversational platform including the website.

Technical requirements:
Should have installed python 3+
Should have rasa installed in the virtual environment(recommended)
Should have a BING map and Zomato API ready for the development -

Installation steps mentioned in the GitHub Readme
Prerequisites:
Should have a good understanding of Python.
Should be aware of CLI commands.
Should have a basic understanding of Machine learning mainly text classification and entity extraction.

https://github.com/colearninglounge/co-learning-lounge/tree/master/Technology/Artificial%20Intelligence/Natural%20Language%20Processing/Concepts/Chatbot/RASA
Slides: https://docs.google.com/presentation/d/1fk0IU1P2r4gvxgN5C-SunUFQeF6aP9Ay0P-D7WbyzmI/edit?usp=sharing

Presenter: Yogesh Kothiya https://www.linkedin.com/in/yogeshkothiya
Yogesh lives in Hyderabad, India. He likes what he does because he does what he likes. That attitude made him drop high paying comfortable job and switched career into AI from fresh. He built multiple complex chatbots for large enterprises at IMIMobile. He also did freelance social chatbot projects and currently running an AI community named Co-learning lounge - https://colearninglounge.com where he along with community members successfully conducted 15+ workshops, a hackathon, and several talks on AI. He loves open-source projects and today he is “RASA superhero” because of active contribution in terms of workshops and blogs.