Book Image

Chatbots for Beginners: A Complete Guide to Build Chatbots [Video]

By : AI Sciences
5 (1)
Book Image

Chatbots for Beginners: A Complete Guide to Build Chatbots [Video]

5 (1)
By: AI Sciences

Overview of this book

Chatbots are computer programs that converse with users, understand their intent, and reply based on preset rules and data. Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering in e-commerce, education, entertainment, finance, health, and more. The course begins with an in-depth introduction to chatbot basics with ML, DL, and AWS. We will understand chatbots, their needs and types, rule-based/self-learning chatbots and their working mechanisms, and explore ML-based chatbot concepts. We will explore Natural Language Toolkit (NLTK) and install packages to create a corpus with Python. We will train and test the chatbot. We will then advance to DL-based chatbots and compare conventional with DL-based chatbots. You will learn about tokenization, encoder-decoder, and implementing RNN-based models. Finally, we will explore AWS for chatbot training with DL. We will examine the features of AWS and build a hotel booking chatbot with Amazon Lex. We will connect AWS Lambda to Amazon Lex and integrate the chatbot with Twilio. We will use AWS SDK and create response cards with chatbots. Upon completion, we will independently be able to build chatbots using ML, DL, and AWS Lex on Python, with a thorough understanding of the creation and functioning of these chatbots. All resources are available at: https://github.com/PacktPublishing/Chatbots-for-Beginners-A-Complete-Guide-to-Build-Chatbots
Table of Contents (4 chapters)
2
Basics of Chatbots with Machine Learning and Python
3
Advanced Chatbots with Deep Learning and Python
4
Chatbots Development with Amazon Lex
Chapter 3
Advanced Chatbots with Deep Learning and Python
Content Locked
Section 23
Deep Learning-Based Chatbot Architecture and Development: Model Completion
In this video, after training and testing the data, querying the questions, and obtaining responses, we are now at model completion with compiling the questions and responses to check for accuracy.