How to Build Chatbot with Python Rasa Open Source

How to Build Chatbot with Python & Rasa Open Source, available at $74.99, has an average rating of 4.13, with 43 lectures, based on 122 reviews, and has 691 subscribers.

You will learn about Gain a comprehensive understanding of the principles and concepts behind conversational AI and the role of Rasa in building chatbots. Master the techniques to create powerful Natural Language Understanding (NLU) models using Rasa NLU, enabling your chatbot to comprehend user inputs accurately. Learn how to integrate external APIs to fetch real-time data, demonstrated through the development of a Weather Chatbot that provides weather information. Explore advanced techniques for NLU enhancement, including entity recognition, synonym handling, and context-sensitive intent recognition Develop skills in building interactive and dynamic conversations using Rasa Forms Understand how to design fallback mechanisms for handling user queries that the chatbot doesnt understand Learn how to integrate Rasa with a MySQL database to store and retrieve user data Discover different deployment options and strategies for your Rasa chatbot, including Facebook, Slack, Telegram, Ngrok and Whatsapp Learn how to integrate your Rasa Chatbot with Flask Web Application This course is ideal for individuals who are Beginner who are interested building Chatbots with Python It is particularly useful for Beginner who are interested building Chatbots with Python.

Enroll now: How to Build Chatbot with Python & Rasa Open Source

Summary

Title: How to Build Chatbot with Python & Rasa Open Source

Price: $74.99

Average Rating: 4.13

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a comprehensive understanding of the principles and concepts behind conversational AI and the role of Rasa in building chatbots.

Master the techniques to create powerful Natural Language Understanding (NLU) models using Rasa NLU, enabling your chatbot to comprehend user inputs accurately.

Learn how to integrate external APIs to fetch real-time data, demonstrated through the development of a Weather Chatbot that provides weather information.

Explore advanced techniques for NLU enhancement, including entity recognition, synonym handling, and context-sensitive intent recognition

Develop skills in building interactive and dynamic conversations using Rasa Forms

Understand how to design fallback mechanisms for handling user queries that the chatbot doesnt understand

Learn how to integrate Rasa with a MySQL database to store and retrieve user data

Discover different deployment options and strategies for your Rasa chatbot, including Facebook, Slack, Telegram, Ngrok and Whatsapp

Learn how to integrate your Rasa Chatbot with Flask Web Application

Who Should Attend

Beginner who are interested building Chatbots with Python

Target Audiences

Beginner who are interested building Chatbots with Python

Welcome to the comprehensive course, “How to Build Chatbot with Python and Rasa Open Source.” If you’re interested to dive into the exciting world of conversational AI, this course is your gateway to creating powerful, intelligent chatbots from scratch using Python and Rasa Open Source.

Course Description:

In today’s digital landscape, chatbots have become an integral part of user engagement and customer support. This course is meticulously designed to equip you with the skills needed to creat sophisticated chatbots using Python and also leverage the capabilities of Rasa Open Source, a leading conversational AI framework.

Key Focus Areas:

1. Understanding Rasa NLU: Lay the foundation by grasping the core concepts of Rasa NLU (Natural Language Understanding). Learn how to preprocess and extract meaning from user messages, forming the basis for accurate interactions.

2. Building Weather Chatbot and API Calls: Dive into practical implementation as you build a Weather Chatbot that interacts with external APIs to provide real-time weather information. Understand how to integrate API calls seamlessly into your chatbot flow.

3. Advanced NLU Techniques: Elevate your chatbot’s understanding of user intent and entities. Explore advanced techniques to enhance NLU performance, including entity recognition and synonym handling.

4. Rasa Forms: Discover the power of Rasa Forms in creating structured conversations. Implement dynamic forms that guide users through complex interactions, ensuring a seamless and user-friendly experience.

5. Fallback and Human Handoff: Learn how to gracefully handle user queries that fall outside the chatbot’s capabilities using fallback mechanisms. Explore strategies for seamless transition to human agents when necessary.

6. Rasa and MySQL Database: Integrate Rasa with MySQL databases to store and retrieve user-specific data, enabling personalized interactions and a more engaging user experience.

7. Deploy Rasa Chatbot: Take your chatbot from development to deployment. Explore deployment options and strategies, ensuring your chatbot is accessible to users across various platforms.

8. Rasa and Flask Integration: Extend the capabilities of your chatbot by integrating it with Flask, a powerful web framework. Learn how to create a user-friendly web interface for your chatbot.

By the end of this course, you’ll gain the skills and knowledge to create intelligent chatbots that can understand user intents, retrieve and present information, and provide a delightful conversational experience. Whether you’re a developer looking to enter the world of AI-driven chatbots or a professional aiming to enhance customer interactions, this course empowers you to build and deploy sophisticated chatbots with Python and Rasa Open Source. Enroll now and embark on a journey into the exciting realm of conversational AI.

Course Curriculum

Chapter 1: Section Introduction

Lecture 1: Course Introduction

Lecture 2: Course Requirements

Chapter 2: Introduction to Rasa and Conversational AI

Lecture 1: Conversational AI & Rasa

Lecture 2: Rasa Overview and It is Key Components

Lecture 3: Setting Up the Development Environment

Lecture 4: Create Rasa Project

Lecture 5: Rasa Project Structure

Chapter 3: Understanding Rasa NLU

Lecture 1: Hello World Chatbot From Scratch

Lecture 2: Overview of Rules.yml File

Lecture 3: Overview of NLU File

Lecture 4: Overview of Domain File

Lecture 5: Overview of Story File

Lecture 6: Entity Extraction

Lecture 7: Adding More Functionality

Chapter 4: Building Weather Chatbot and API Calls

Lecture 1: Creating Intent and Entities for Weather Bot

Lecture 2: Rasa Pipeline

Lecture 3: What are Slots

Lecture 4: Rasa Policies

Chapter 5: Advanced NLU Techniques

Lecture 1: Rasa Synonyms

Lecture 2: Entity Extraction with Regex

Lecture 3: Rasa Lookup Table

Lecture 4: Movie Recommendation Chatbot Part One

Lecture 5: Movie Recommendation Chatbot Part Two

Lecture 6: Choosing Right Pipeline in Rasa

Chapter 6: Rasa Forms

Lecture 1: Rasa Form Introduction

Lecture 2: Rasa Forms Practical Example

Chapter 7: Fallback and Human Handoff

Lecture 1: Fallback and Human Handoff

Lecture 2: Fallbacks in Rasa

Lecture 3: NLU Fallback

Lecture 4: Handling Low Action Confidence

Lecture 5: Two Stage Fallback

Chapter 8: Rasa and MySQL Database

Lecture 1: Creating Basic Chatbot

Lecture 2: Adding Data to Database

Lecture 3: Show All Data

Chapter 9: Deploy Rasa Chatbot

Lecture 1: Connect Rasa to Ngrok

Lecture 2: Integrate Rasa Chatbot with Facebook Messenger

Lecture 3: Rasa Chatbot Integration with Slack

Lecture 4: Rasa and Telegram Integration

Lecture 5: Integrate Rasa Chatbot with WhatsApp

Chapter 10: Rasa and Flask Integration

Lecture 1: Section Introduction

Lecture 2: Create Flask and Rasa Chatbot

Lecture 3: Handle User Input in Chatbox

Lecture 4: Send User Message to the Server

Instructors

Parwiz Forogh
Programmer

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 16 votes

4 stars: 41 votes

5 stars: 58 votes

Frequently Asked Questions

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