Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you.
The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment.
The final chapter of
Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn
Table of ContentsChapter 1: Introducing Chatbots
Chapter Goal: Make the user get familiar with Chatbots.Sub -Topics1. Do’s and Don'ts in Chatbots2. What are the limitations of chatbots and how we should solve them?3. What are different kind of chatbots ? Where do they fit in ?
Chapter 2: Natural Language Processing
Chapter Goal: Be able to do custom natural language processing platform for your chatbotsSub - Topics 1. Installation of NLTK and methods in natural language processing.2. POS Tagging, Stemming, Lemmetization, 3. Logical Semantics
Chapter 3: Chatbot Development
Chapter Goal: Building a chatbot and defining its data constraintsSub - Topics: 1. Using api.ai platform to create a chatbot2. Feeding data and defining Intents and entities
Chapter 4: Chatbot Communication
Chapter Goal: Enabling communication with the bot to make the bot respond to your queries.
Sub - Topics:
1. Making our chatbot respond to our queries2. Integration and Deployment
Chapter 5: Build-Train-Deploy
Chapter Goal: To build, train and deploy a chatbot of your own Sub - Topics: 1. Getting acclimatize to use open source libraries to train your data2. Defining Intents and entities on your data3. Using ML algorithms to predict the intent and take action based on that4. Using your code in a web app to make a conversational agent.5. Deploy your app on your own server with AWS
