Basics of Chatbots with Machine Learning & Python

1. Course and Instructor Introduction

This video introduces the author/instructor and outlines his experience and qualifications as an instructor in this online course content discipline.

2. AI Sciences Introduction

This video outlines the background of the AI Sciences group, its core competencies, and the expertise they bring to the courses.

3. Course Description

In this video, we will explore the course's prerequisites and discuss the course content and the concepts that will be covered in this course.

4. ML-Based Chatbots

This video overviews the machine learning-based chatbot's general architecture, purpose, ML-enabled features, and ML revolutionaries.

5. Conversational Chatbots

In this lecture, we will understand conversational chatbots and briefly examine the features of a conversational chatbot, its installation, and its development.

1. Module Overview

This video provides an overview of the concepts to be learned in this module. You will learn about chatbots and what they are used for and overview them in general.

2. History of Chatbots

This video elaborates on the history of chatbots that predated the 60s and 70s era of technology. We will understand when chatbots were first created and how they have gradually progressed to become the present versions of chatbots.

3. Applications of Chatbots

In this lesson, you will learn about the different applications of chatbots, including helpdesk assistants, tutors, home assistants, office assistants, phone assistants, and medical assistants.

4. Chatbots Versus Virtual Assistants Versus Personal Assistants

This lecture compares chatbots, virtual assistants, and personal assistants; delves into the three types of traits; and brings out their advantages and disadvantages to the forefront.

5. Benefits of Chatbots

This video will elaborate on the benefits of using chatbots and the need for adopting chatbots, including personalized customer experience, efficient staff onboarding, standardized solutions, cost saving, and 24x7 availability.

6. Why Should Companies Pick Chatbots

Here, we will discuss why organizations adopt chatbots, including simplicity of functioning, efficiency and speed, improving experience, personalization, and sustainability.

7. Chatbot Types

In this lesson, we will look at classifying chatbots based on how the chatbots can be trained and tested, including the rule-based chatbot-where rules are specified-and self-learning chatbots, where the chatbot learns on its own with the data fed into it.

8. Rule-Based Chatbots

Let's understand what rule-based chatbots are and their mechanism of functioning in detail. Here, certain rules or criteria are provided as support to the chatbots, and the tasks performed, and the responses are based on those rules.

9. Self-Learning Chatbots

Here, we will understand self-learning chatbots, which, as suggested, means that the chatbots can learn independently. The chatbot asks a series of queries and stores the responses before deciding on an action based on the responses.

10. Mechanism of Chatbots

Let's understand the mechanism of how a chatbot works. It needs a predefined goal or objective to function, draft potential solution flows, validate examples, define the solution flows, and launch the chatbot.

11. Challenges of Chatbots

In this video, we will understand the challenges we face using a chatbot, including emerging trends, security and privacy, customer saturation, predicting human behavior, user satisfaction, and user-bot communication.

12. Quiz

This video is a quiz/exercise based on the concepts learned in this section so far.

13. Quiz Solution

This video is the solution to the quiz/exercise based on the concepts learned in this section.

1. Module Introduction

Let's look at the different concepts you will be learning in this specific module that focuses entirely on machine learning-based chatbots, including purpose, and finally working on a project at the end.

2. Module Overview

In this video, we will outline the concepts, including the general architecture of machine learning, the purpose of chatbots, the Natural Language Toolkit (NLTK), and developing a rule-based chatbot using NLTK.

3. Architecture of ML Chatbots

Here, we will focus on and simplify the machine-learning architecture of chatbots with the help of an example. We will understand the NLTK library and its role in developing chatbots.

4. ML Enabled Features

In this lesson, we will explore the enabling features of a machine learning-based chatbot, including scaling operations, task automation, user engagement, social media interaction, multilingual models, and connectivity.

5. Revolution with ML

In this video, we will look at the various technologies in which machine learning has revolutionized, including customer services, eCommerce, healthcare, travel and tourism, banking and finance, and general services.

6. NLTK Features

Let's explore the Natural Language Toolkit, a library used for specific tasks related to natural language processing, including tokenization, filtering words, stemming, lemmatization, chunking/chinking, and entity recognition.

7. Rule-Based Chatbots

This video overviews the steps in developing rule-based chatbots, including installing the package, inputting data, adding word tokens, lemmatizing, and response generation.

8. Package Installation

This video demonstrates the steps involved in installing NLTK. Another package we will need to install for creating our chatbot is the Wikipedia app and then importing the required libraries.

9. Data Input

In this lesson, we will begin inputting the data we will use for our rule-based chatbot.

10. Word Tokens and Remove ASCII

In this video, you will learn to add word tokens using the normalize function and develop the word token. We are going to use the NLTK.word_tokenize function.

11. Remove Tags and Lemmatize

In this video, we will look at removing tags or symbols that we do not want to use in our word token development process. We will then lemmatize the word token using the tag map lemmatizer.

12. Chatbot Greets

After creating the word tokens, removing the tags, and lemmatizing the words, we will create the chatbot's welcome responses or welcome inputs.

13. Response Generation

In this video, we will define a generateResponse() function and define the expected user response from the chatbot.

14. Wiki Search

In this video, you will learn how to search Wikipedia. We will define a function to input data and obtain a response from the Wikipedia_data input we created.

15. Developing Results

We will now test the chatbot for response generation and verify that the chatbot is working perfectly. We will assess the results based on the data input and the response.

16. Local Search and Wikipedia Search

In this lesson, you will learn how to conduct a local search versus a Wikipedia search. We will generate results based on the responses from the Wikipedia search and the local search.

1. Module Introduction

This is a brief introductory video regarding the concepts learned in this module, including preparing and developing the conversational chatbot project using the NLTK library.

2. Project Overview and Packages

In this lesson, we will go over the project conception in detail and install and import all the required libraries for the project, input the data, and perform other tasks to project completion.

3. Getting the Data

In this video, we will look at using the request library to seek out the webpage from which we want the information. In our case, we have chosen the webpage of artificial intelligence.

4. Elimination

After collecting all the data, you will learn to eliminate the text that we do not want to use in the chatbot project through an elimination process.

5. Tokenization

In this lesson, we will perform two types of tokenization-sentence tokenization and word tokenization-using the NLTK library.

6. Lemmatization and Processed Text

In this video, we will look at the project's next step, the lemmatization process, using the WordNet lemmatizer and the NLTK library for the conversational chatbot.

7. Greeting Function

After setting everything up, we will now look at creating a few greeting inputs and a few greeting responses for the conversational chatbot to respond with.

8. Generate Response

We will import two libraries, tfidf and cosine_similarity, and then use the TF-IDF vectorizer to generate chatbot responses based on the query input.

9. Bot Finishing

After setting all elements of the chatbot, we will complete the bot by inputting queries and data and receiving responses from the chatbot based on the question placed.

10. Testing the Bot

After finishing the bot and making it send responses, we will perform the final step: check the chatbot for accuracy of the responses and verify that the tasks are performed correctly.