Building Chatbots with Google Dialogflow
The course is part of this learning path
This course is the second part of our series on building chatbots using Google Dialogflow. In this course, we'll be taking a hands-on approach to implementing a Dialogflow chatbot, specifically focusing on the graphical user interface. You'll follow along as we cover the example of building an online banking customer service chatbot using Dialogflow.
We'll also discuss the use of databases and other knowledge sources in order to provide meaningful responses that give you the option to serve real-world information to your user.
- Learn what intents are and how to train your chatbot to look for them
- Set up entities in Dialogflow
- Understand how chatbots interact with users and how to test your chatbot
- Learn how to connect your chatbot to a data source
- Understand the user interfaces available with Dialogflow
- Learn how to use the Knowledge service to build chatbots quickly and with less configuration
- Anyone looking to build chatbots using Google Dialogflow
Before taking this course, make sure you've done Part One first. To get the most out of this course, you should also have a basic understanding of:
- Computer science techniques
- REST APIs and SQL
- Google Cloud Platform
And that about wraps it up. As a quick recap Dialogflow, GUI is a great way to quickly build out a highly functional chatbot. This Graphical User Interface is a first-class citizen and you're able to really get all of the parts of the chatbot set up through it. There's no dependency on an API or a CLI tool.
Additionally, with clever features such as entities, actions, and parameters we can have Dialogflow help users ask better questions and participate in a two-way conversation. The built-in integrations in Dialogflow work with many, many different software and hardware platforms allowing you to integrate everything from phones to computers, to websites, and even voice assistance if you want. And on top of all this are a set of new features such as the new Knowledge base feature set which allow you to quickly integrate new data sources without having to create all of the traditional minutiae of making a chatbot.
Anyway, thank you for attending part two of this course and don't forget to rate it. The feedback here is really useful for us to understand what you wanna see next and if you like this type of content. Anyway, thank you and I hope you enjoyed it.
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.