Chatbots with Python and Machine Learning Online Course

Chatbots with Python and Machine Learning Online Course

This course provides a comprehensive introduction to chatbots with Python and machine learning. It covers the basics of chatbots, including rule-based and self-learning types, and explores their application in customer service, information gathering, and request routing. You'll gain a deep understanding of the architecture of ML-based chatbots and their impact. The course includes an overview of the Natural Language Toolkit (NLTK), package installation, corpus creation, text preprocessing, and response generation. You'll learn to implement term-frequency inverse document-frequency (TF-IDF) and train and test rule-based chatbots. The final project involves developing an AI-powered question-answer chatbot using NLTK. By the end of this course, you'll be equipped to design, implement, and evaluate machine learning models for real-time chatbot development across various domains.

Key Benefits

• Gain a foundational understanding of chatbots, including both rule-based and self-learning models, and the architecture of machine learning-powered chatbots.
• Explore the significant impact of machine learning technologies on chatbot development and learn how to leverage the Natural Language Toolkit (NLTK) for enhanced functionality.
• Engage in practical, hands-on experience with term frequency-inverse document frequency (TF-IDF) techniques, and gain proficiency in testing and training chatbots using machine learning methods.

Target Audience

This course is designed for individuals looking to enhance their expertise in applied machine learning, with a focus on mastering data analysis and developing customized chatbots tailored to specific applications. It offers in-depth coverage of machine learning algorithms and their integration into chatbot development. Whether you are interested in rule-based systems or conversational chatbots, this course is ideal for machine learning practitioners, researchers, and data scientists. Prior experience in chatbots or machine learning is not required, though a foundational understanding of Python (at a basic to intermediate level) is essential, as Python coding is not covered separately in the course.

Learning Objectives

• Understand the different types of chatbots, including rule-based and self-learning models, and their respective functionalities.
• Master text preprocessing techniques and develop essential helper functions using Python for chatbot development.
• Gain an in-depth understanding of the Natural Language Toolkit (NLTK) and its impact on chatbot functionality and performance.
• Acquire hands-on experience in generating text using Python, allowing you to develop and enhance chatbot capabilities.
• Learn the process of testing and training chatbots with machine learning techniques, ensuring optimal performance.
• Implement and practice the term-frequency times inverse document-frequency (TF-IDF) technique to refine chatbot response generation.

Course Outline

The Chatbots with Python and Machine Learning Exam covers the following topics - 

Module 1 - Introduction

• Course and Instructor Overview
• Introduction to AI Sciences
• Course Outline and Description
• Machine Learning-Based Chatbots Overview
• Understanding Conversational Chatbots

Module 2 - Chatbots Overview

• Module Introduction
• History and Evolution of Chatbots
• Applications Across Industries
• Comparison: Chatbots, Virtual Assistants, and Personal Assistants
• Key Benefits of Chatbots
• Why Companies Should Adopt Chatbots
• Types of Chatbots: Rule-Based and Self-Learning
• How Chatbots Work: Mechanisms and Functionality
• Challenges in Chatbot Development

Module 3 - Machine Learning-Based Chatbots

• Module Introduction and Overview
• Architecture and Design of ML Chatbots
• Features Enabled by Machine Learning
• The Role of Machine Learning in Chatbot Innovation
• Leveraging NLTK for Chatbot Development
• Building Rule-Based Chatbots with ML Features
• Package Installation and Setup
• Data Preparation: Input and Preprocessing
  ○ Word Tokenization and ASCII Removal
  ○ Tag Removal and Lemmatization
• Chatbot Functionalities:
  ○ Greeting Users
  ○ Generating Responses
  ○ Wiki Search and Result Compilation
  ○ Local and Wikipedia Searches

Module 4 - Project: Developing a Conversational Chatbot with Machine Learning

• Module Introduction and Project Overview
• Required Packages and Setup
• Data Acquisition and Preparation
  ○ Data Cleaning and Elimination
  ○ Tokenization and Text Lemmatization
• Implementing Chatbot Functionalities:
  ○ Greeting Functionality
  ○ Response Generation
  ○ Finalizing the Bot Framework
• Testing and Evaluating the Chatbot