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Chatbot Building with Rasa
Rating: 3.3 out of 5(90 ratings)
499 students

Chatbot Building with Rasa

Rasa NLU, Rasa Core - How to build a Facebook Massenger Chatbot
Created byGoTrained Academy, Faizan Ali
Last updated 6/2023
English

What you'll learn

  • Understanding concepts of building chatbots with Rasa NLU, Rasa Core, DialogFlaw & Wit•ai
  • Building chatbots for Facebook Messenger
  • Buiding a chatbot that answers FAQs
  • Deploying your chatbot in Heroku application platform

Course content

2 sections • 15 lectures • 2h 38m total length

  • Rasa Installation and Setup7:14

    Install and configure Rasa NLU for chatbot development. Create a virtual environment, manage dependencies with a requirements file, and use spaCy with sklearn to extract intents and entities.

  • Rasa - Preparing Training Data & Training Model16:06

    Prepare training data for a rasa chatbot by creating and updating nlu.md, configuring the spacy_sklearn pipeline in nlu_config.yml, and training the model using a training_data.json with rasa nlu trainer.

  • Rasa Interpretation Webhook Setup5:19

    Train a model with training phrases, interpret intents and entities using the Rasa NLU interpreter, and build a Flask webhook using pymessenger to serve a chatbot.

  • Facebook Application Setup11:45

    Set up a Facebook app and page for the currency converter, obtain a page access token, and configure webhooks with a local ngrok tunnel, verifying tokens and handling messenger posts.

  • Facebook Echo Setup6:38

    Set up the Ecobot on Facebook using Rasa by parsing Messenger events and messages, mapping sender and recipient IDs, and sending back text replies via the Facebook Messenger platform.

  • Rasa - Interpreting Intents & Entities15:41

    Leverage Rasa NLU to interpret intents and entities, including a greet intent, and implement a currency conversion workflow using Fixer.io API to fetch rates and compute conversions.

  • Rasa - Currency Conversion Chatbot10:11

    Develop a currency conversion feature in a rasa chatbot by parsing api responses with json.loads, using rates to convert from one currency to another, and preventing division by zero.

  • Code Files: Final Rasa NLU Chatbot0:06

    Download the final code files of the Rasa NLU chatbot.

Requirements

  • Its necessary to have basic programming knowledge of Python

Description

Do you want to create a talking chatbot that interacts with your visitors? In this tutorial, you will learn how to create Python chatbots using Rasa NLU and Rasa Core. They provide several Natural Language processing functions that parse user input and match it to the right response. Integrating NLP into your bot can be difficult, but with Rasa, it is much easier to create a Facebook Messenger bot or a website chatbot.


Rasa is a powerful open-source machine learning framework for developers to create contextual chatbots and expand bots beyond answering simple questions. In this course, you will study both Rasa NLU and Rasa Core.


  • Rasa NLU is an open-source natural language processing tool for intent classification and entity extraction in chatbots. You can think of it as a set of high-level APIs for building your own language parser using existing NLP and ML libraries. Among the main reasons for using open-source NLU are: 1) you don’t have to hand over all your chatbot training data to Google, Microsoft, Amazon, or Facebook; 2) Machine Learning is not one-size-fits-all. You can tweak and customize Python chatbot models for your training data; and 3) Rasa NLU runs wherever you want, so you don’t have to make an extra network request for every chatbot message that comes in.


  • Rasa Core leverages developers’ existing domain knowledge to help them bootstrap from zero training data, and adopts an interactive learning approach. With Rasa Core, you manually specify all the things your bot can say and do. We call these actions. One action might be to greet the user, another might be to call an API, or query a database. Then you train a probabilistic model to predict which action your Python chatbot should take given the history of a chatbot conversation.


This Python chatbot course will help you:

  • Build chatbots with Python using Rasa NLU & Rasa Core

  • Understand intents and entities.

  • Build a Facebook Messenger bot.

  • Deploy chatbots on cloud platforms such as Heroku.



Who this course is for:

  • Software Python developers looking to build chatbots for their websites and mobile apps
  • Developers of Facebook looking to build Massenger chatbots
  • Development professionals and students looking to learn how to use Rasa NLU, Rasa Core, DialogFlow and Wit-AI to build chatbots.

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