In this chapter, you'll learn how to build your first chatbot. After gaining a bit of historical context, you'll set up a basic structure for receiving text and responding to users, and then learn how to add the basic elements of personality. You'll then build rule-based systems for parsing text.

Introduction to conversational software

EchoBot I

EchoBot II

Creating a personality

Chitchat

Adding variety

ELIZA I: asking questions

Text processing with regular expressions

ELIZA II: Extracting key phrases

ELIZA III: Pronouns

ELIZA IV: Putting it all together

Chatbots 101

Here, you'll use machine learning to turn natural language into structured data using spaCy, scikit-learn, and rasa NLU. You'll start with a refresher on the theoretical foundations and then move onto building models using the ATIS dataset, which contains thousands of sentences from real people interacting with a flight booking system.

Understanding intents and entities

Intent classification with regex I

Intent classification with regex II

Entity extraction with regex

Word vectors

word vectors with spaCy

Intents and classification

Intent classification with sklearn

Entity extraction

Using spaCy's entity recognizer

Assigning roles using spaCy's parser

Robust language understanding with rasa NLU

Rasa NLU

Data-efficient entity recognition

Understanding natural language

In this chapter, you'll build a personal assistant to help you plan a trip. It will be able to respond to questions like "are there any cheap hotels in the north of town?" by looking inside a hotel’s database for matching results.

Virtual assistants and accessing data

SQL basics

SQL statements in Python

Exploring a DB with natural language

Creating queries from parameters

Using your custom function to find hotels

Creating SQL from natural language

Incremental slot filling and negation

Refining your search

Basic negation

Filtering with excluded slots

Building a virtual assistant

Everything you've built so far has statelessly mapped intents to actions and responses. It's amazing how far you can get with that! But to build more sophisticated bots you will always want to add some statefulness. That's what you'll do here, as you build a chatbot that helps users order coffee.

Why statefulness is key

Form filling

Asking contextual questions

Dealing with rejection

Asking questions & queuing answers

Pending actions I

Pending actions II

Pending state transitions

Putting it all together I

Putting it all together II

Frontiers of dialogue research

Generating text with neural networks

Congratulations!

Dialogue

ATIS (Airline Travel Information System)

Hotels database

Messaging and voice-controlled devices are the next big platforms, and conversational computing has a big role to play in creating engaging augmented and virtual reality experiences. This course will get you started on the path toward building such applications. There are a number of unique challenges to building these kinds of programs, like how do I turn human language into instructions for machines? In this course, you'll tackle this first with rule-based systems and then with machine learning. Some chat systems are designed to be useful, while others are just good fun. You will build one of each and put everything together to make a helpful, friendly chatbot. Once you complete the course, you’ll also learn how to  connect your chatbot to Facebook Messenger!

Introduction to Natural Language Processing in Python

Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

Building Chatbots in Python

Co-founder and CTO, Rasa

Supervised learning

ATIS dataset

ATIS dataset II

Nearest neighbor classification

Nearest neighbor classification in scikit-learn

Support vector machines

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_3631/subtitles/course_3631_11a1d86a77f8572714714ad6ba5323b8.vtt

Understanding natural language - Intents and classification

Intents and classification

Create Your Free Account