Complete Chatbot Course Using Rasa Framework & Python

Name: Complete Chatbot Course Using Rasa Framework & Python
Rating: 3.7 (327 reviews)

Learn to Deploy advanced chatbots on Facebook and Telegram using the Deep Learning & NLP powered by Rasa Framework!

Created byMohammed Murtuza

Last updated 2/2022

English

What you'll learn

Build a Chatbot from Scratch using Python
Learn about the Rasa Framework
Deploy your chatbot on the Cloud
Connect your chatbot to Facebook, Telegram
Learn how to use Custom Models
Learn Rasa NLU to understand user inputs
Learn Rasa Core for Dialogue Management of the Users
Learn how to handle Small talk
Learn how to use in-built Deep learning models or Bring your own Model in Rasa NLU
Map your domain to your Rasa Server

Course content

17 sections • 77 lectures • 7h 49m total length

Why Chatbots7:18
In this lecture, we will look at some of the reasons as to why should we build chatbots.
Why Rasa?3:07
In this lecture, lets take a look at why should we Rasa as our Chatbot development platform, when we have quite a few other options available.
Whats Rasa?4:59
In this video, We will begin by understanding what exactly is the Rasa Framework.
Installing Python and Rasa on Windows7:26
In this video, we will download and install Python, Rasa.

1. Introduction - RASA Natural Language Understanding (NLU) - Intents & Entities4:07
In this lecture, lets understand whats Rasa NLU and what are Intents & Entities extracted by it.
2. Writing Intents & Entities Examples - Training Data - NLU7:55
In this video, we'll look at the format of training data for intent classification and entity extraction. We'll be creating our First file in the Rasa Framework!
3. Rasa NLU Pipeline Introduction2:08
In this video, you will be introduced to the Rasa NLU Pipeline which is a sequence of steps used to process the input user data and subsequently extract intents and entities.
4. Defining NLU Pipeline in Config File10:06
In this lecture, we will create a config file where we define our NLU Pipeline sequence.
5. Training our first Rasa NLU model6:33
In this lecture, we'll train our first RASA NLU model through the command line!
6. Rasa NLU Entity Synonyms & Lookup Tables Part 19:13
Explore how to use rasa nlu entity synonyms and lookup tables to map varied terms like mobile and phone to a single category, train the model, and test mappings.
7. Rasa NLU Entity Synonyms & Lookup Tables Part 25:08
In this video, we'll continue on the previous lecture and take a look at Lookup Tables.

1. Introduction to Custom Components in Rasa NLU4:39
In this video, we'll get introduced to custom components.
2. Introduction to Transfer Learning & Pre-trained Word embeddings7:12
In this lecture, we will look whats transfer learning and how we can use pre-trained word embeddings in our Rasa NLU Pipeline.
3. Training an Embedding Model Word2Vec20:18
In this video, we'll train a Word2Vec model using user reviews for mobiles and laptops scraped from amazon.com
4. Using Custom Word2Vec Pre-trained model in the RASA NLU Pipeline7:25
In this lecture, we'll use the pre-trained custom word2vec model in our Rasa NLU pipeline.
5. Introducing Custom Component Class - Profanity Filter Part 117:33
In this lecture, we'll look at how we can introduce a custom component in Rasa NLU using the Custom Component Class.
6. Introducing Custom Component Class - Profanity Filter Part 21:06
In this lecture, we'll continue to look at how we can introduce a custom component in Rasa NLU using the Custom Component Class.
Defining Give Information Intent5:23

1. Introduction to Rasa Core6:08
In this lecture, we will look whats Rasa Core.
2. Defining Custom Actions4:02
In this lecture, we will look at how to define custom action using python.
3. Rasa Core - Stories5:35
In this lecture, we'll look at stories which are used for training our Rasa Core Model.
4. Rasa Core - The Domain10:22
In this video, we'll look at the domain file where we define everything the bot needs to know.
5. Defining Stories & Running the Action Server Demo11:41
In this video, we'll write some example stories and train our first Rasa Core model and also run the actions server.
Rasa Core - Rules4:00

1. Rasa Core - Introduction to Dialogue Policies4:52
In this video, we'll look at what are Dialogue Policies.
2. Types of Dialogue Policies - Memoization & Mapping Policy4:00
In this video, we'll look at the types of dialogue policies starting with memoization & mapping policies.
3. Types of Dialogue Policies - Machine / Deep Learning & Fallback Policy9:48
In this lecture, we'll continue looking at dialogue policies - Machine/Deep Learning & Fallback policies.
4. Dialogue Policies Priority & Ranking2:20
In this lecture, we will look at Dialogue Policies ranking.
5. Defining Policies in Config File2:02
In this video, we'll quickly take a look at how to define dialogue policies in the config file.

1. Rasa Slots - Introduction2:40
In this lecture, we will take a look at Slots.
2. Type of Slots6:20
In this lecture, we'll take a look at the type of Slots.
3. Setting & Getting Slots Part 13:06
In this video, we will look at how to set slots and also use the slots which are set.
3. Setting & Getting Slots Part 22:50
In this video, we'll continue to look at how to set slots and also use the slots which are set.
5. Slots - Things to Remember2:02
In this video, we'll look at some important things to keep in mind for slots.

1. Introduction to Forms Part 14:23
In this video, we'll take a look at what are forms.
1. Introduction to Forms Part 27:34
In this video, we'll continue to take a look at what are forms.
Introduction to Forms in Rasa v2.0/3.0 [Updated]8:45
3. Adding a Form to our Chatbot Part 119:05
3. Adding a Form to our Chatbot Part 211:27
5. Adding Forms to our Chatbot and Intro to Rasa X Part 37:22
6. Handling Single Words or Number Entities Using Form Part 110:12
Learn to handle single word or number inputs in Rasa forms by using custom slot mappings and validate methods to extract integers from any user input, removing inbuilt entity recognition.
7. Handling Single Words or Number Entities Using Form Part 25:43
8. Conditional Slots - Adding Laptops Category - Part 19:10
Learn how to implement conditional slots in a Rasa chatbot by adding a laptops category and changing questions for battery backup and storage capacity, with domain updates and validation.
9. Conditional Slots - Adding Laptops Category - Part 22:34
Train a model in the RasaX interface, add featurized slots and utter ask templates for laptops, test a laptops-focused form, and note plans to query an sql database for devices.

1. Adding Buttons - Multiple Search - Reset Slots Part 13:19
In this lecture, we will start improving our bot by adding an easy option for multi search by adding buttons.
2. Adding Buttons - Multiple Search - Reset Slots - Intro to Debugger Part 23:33
In this video, we will continue improving our bot and get introducted to the inbuilt Rasa debugger.
3 .Adding Buttons - Multiple Search - Reset Slots - Intro to Debugger Part 33:05
Demonstrates using the debugger to inspect slots, intents, and next actions while testing a bot. Shows button-driven searches and slot resets with a user action menu.

1. Rasa - Handling Small Talk - Introduction7:51
2. Rasa - Handling Small Talk Example Part 13:23
Create an ask residence intent in Rasa to handle where you live questions, add example phrases, and connect to a custom action with an utter residence template.
3. Rasa - Handling Small Talk Example Part 23:32
Define template utter residence and map it to the action your residence; use user utterance reverted and action reverted events to revert dialogue flow under mapping policy and train model.
4. Rasa - Handling Small Talk Example Part 36:23

Requirements

Basic knowledge of Python
Basic Programming or IT Knowledge

Description

Updated 2022!!

The most in-demand skill for any Data Scientist right now is creating a chatbot to handle conversations with user. With nearly 80% companies expected to implement chatbots in the near future. Do you want to be the one implementing it? The course is designed to teach you how to create a chatbot which can help users with suggestions of laptops and phones from amazon right from the creation of first file to the deployment on platforms like Facebook and Telegram.

Learn the most flexible and fastest growing Chatbot Framework out there! Rasa is an open source framework which doesn't charge you anything! What makes Rasa standout when compared to all the bots out there is its flexibity, it provides very solid inbuilt frameworks with options to customize the entire chatbot module. Keep all your User data on your cloud premise without sharing with any third parties (which is really important given the current data policies and growing privacy concerns!).

With very little coding needed any beginner in programming can get along with the course and learn how to build advanced chatbots!

The course has given major emphasis on practicality and application of all the concepts taught. With a blend of slides and code walkthrough you are taken through a complete journey of deploying your first chatbot.

The course comes with all the source code used in developing the bot. With the intention of prompt updates to the course as and when needed you can be sure you never miss out on new features added to the Rasa Framework! You surely do not want to miss this!

Who this course is for:

Beginner Data Scientists
Experienced Data Scientists
Software Engineers
People interested in the latest cutting-edge Chatbot technologies
People with beginner python or coding knowledge

Complete Chatbot Course Using Rasa Framework & Python

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 23min

Rasa Natural Language Understanding (NLU)7 lectures • 45min

Rasa NLU - Custom Components7 lectures • 1hr 4min

Introduction to Rasa Core6 lectures • 42min

Dialogue Policies5 lectures • 23min

Slots - Bots Memory5 lectures • 17min

Forms10 lectures • 1hr 26min

Fallback Policy2 lectures • 16min

Section 5 - Improving the Bot - Add Buttons - Multi Search - Reset Slots - Debug3 lectures • 10min

Handling Small Talk4 lectures • 21min

Requirements

Description

Who this course is for: