Chatbots are software applications used for online chat conversations through text or text-to-speech instead of providing direct contact with a live human agent. Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering.
This course begins with a brief overview of chatbots, their need, and the types of chatbots. We will explore rule-based versus self-learning chatbots. We will understand the working mechanism of chatbots. We will explore machine learning-based chatbots and understand the ML-based architecture of chatbots. You will learn about the purpose of ML-based chatbots and their impact. We will get an overview of the Natural Language Toolkit (NLTK). You will learn to install packages and create a corpus with Python. We will delve into text preprocessing and helper function deployment, generate responses, and implement term-frequency times inverse document-frequency. We will train and test rule-based chatbots and finally work on a project developing an artificial intelligence question-answer chatbot using NLTK.
Upon course completion, you will be able to relate the concepts and theories for chatbots in various domains, understand and implement machine learning models for building real-time chatbots, and evaluate machine learning models in chatbots.
All resources are available at: https://github.com/PacktPublishing/Basics-of-Chatbots-with-Machine-Lear…
publication date
2023-02-24
what you will learn
Learn about chatbot types, rule-based and self-learning chatbots
Learn text preprocessing and develop helper functions with Python
Explore the impact and overview of the Natural Language Toolkit
Gain hands-on practice, generate text in Python to develop chatbots
Explore testing and training of chatbot with machine learning
Implement term-frequency times inverse document-frequency hands-on
key features
Learn chatbot basics, rule- and self-learning chatbots, and chatbot machine-learning architecture * Explore machine learning technology’s impact on chatbots and Natural Language Toolkit (NLTK) * Implement hands-on term/inverse document-frequency, chatbot testing/training with machine learning
approach
The course is designed to help you understand concepts easily and provides a unique hands-on experience in developing complete chatbots for your customized dataset using various projects. This well-structured learning-by-doing course will help you master the concepts and methodology efficiently. This course is easily understandable, expressive, self-explanatory, and concise, with live coding.
audience
This course delivers content to people wishing to advance their skills in applied machine learning, master data analysis with machine learning, build customized chatbots for their applications, and implement machine learning algorithms for chatbots. This course is for you if you are passionate about rule-based and conversational chatbots. Machine learning practitioners, research scholars, and data scientists can benefit from the course. No prior knowledge of chatbots, or machine learning, is needed. You will need to know basic to intermediate Python coding, which is not taught separately in the course.
short description
Let’s learn the basic concepts for developing chatbots with machine learning models. This compact course will help you learn to use the power of Python to evaluate your chatbot datasets based on conversational notes, online resources, and websites. Garner hands-on practice in text generation with Python for chatbot development.
subtitle
Let's explore chatbots and develop conversational chatbots with machine learning
keywords
self-learning chatbots, machine learning architecture, chatbot development, text preprocessing, Natural Language Toolkit (NLTK)
Product ISBN
9781837635092
