Build Chatbots with Watson Assistant


In this course, you’ll explore the Watson Conversation service in depth. You’ll watch videos that explain conversational concepts and apply them to build a chatbot. You will complete seven hands-on labs, culminating in the creation of your own fully functional chatbot.
Intro Video:

About this course

Just a walk in the park! In this course, create your own chatbot using the IBM Watson Assistant service. Your chatbot will simulate a conversation around some of the coolest US national parks, such as Arches and Yellowstone. You’ll learn to train your bot, create its dialog, and write code to integrate with external APIs.

Want to know what the weather will be like in Denali? Who wouldn’t? You’ll integrate the IBM Weather Company Data service into your chatbot.

With the IBM® Watson™ Assistant service, you can create an application that understands natural-language input and uses machine learning to respond to users in a way that simulates a conversation between humans.

Watson Assistant uses concepts like intents, entities, and dialog to help you craft powerful conversational experiences. You’ll learn about these concepts and how to apply them by building a chatbot. You’ll also learn how to create conversations declaratively (without code) and programmatically by using the Watson Assistant tooling and Watson Developer Cloud SDK.

Please note: This version of the course has references to “Watson Conversation”. This service has been recently renamed to “Watson Assistant”.

Interact with the working version of the national parks application below and try these phrases:

  • “Tell me about animals in Denali”
  • “Tell me about Zion National Park”

You can also ask “What can you do?” to see a set of options.

Course outline

    • Get started
    • Lab 1: create a Watson Conversation instance
    • Intents
    • Lab 2: import and create intents
    • Entities
    • Lab 3: import and create entities
    • Dialog
    • Lab 4: create a dialog flow (conversation)
    • Jump to action
    • Watson Developer Cloud SDK
    • Lab 5: interact with the app programmatically
    • Lab 6: enhance the sample application
    • Integrate the weather service introduction
    • Lab 7: integrate the weather service
    • Summary

Prerequisite skills

  • None

Prerequisite software



  • The minimum passing grade for the course is 70%. Review questions are worth 50%, and the final exam is worth 50% of the total course grade.
  • You get 1 attempt to take the exam with multiple attempts per question.

Course Staff

Ant Cole

Carmine Dimascio

Carmine is a Watson Education – Senior Software Engineer.

Course Content

Total learning: 1 lesson Time: 5 hours

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