Building Chatbots with Google Dialogflow: Part 2
Building Chatbots with Google Dialogflow
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
Hello everyone and welcome back to part two of the course on 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.
As this is part two, if you're unfamiliar with Dialogflow, I recommend you go back check out part one and keep watching straight through part two. Apart from the GUI walkthrough in this class, we'll also be discussing 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.
Although most of this class is introductory level, we assume you have a baseline understanding of the components of Dialogflow and it's also helpful to understand specific components such as Rest APIS and SQL. In general, Dialogflow has a heavy dependency on Google Cloud considering it's natively integrated so understanding concepts from Google Cloud serverless architecture is helpful such as what's a function.
Much like part one of this class, part two is targeted at people who wanna learn how to make a chatbot in a very practical sense. The second part in particular though, however, takes a very developer-centric approach and if you are someone who wants to learn how to make chatbots in an immediate sense, such as you can exit this class and start making your own, this is ideal for you.
And before we get started a little bit about me again. This is a repeat if you've seen part one. My name is Chris Gambino. I'm one of the co-founders and lead architects at Calculated Systems. Before working at Calculated Systems, I worked at Google Cloud and Hortonworks where I was designing and implementing big data systems and additionally some of my recent projects have me working on high volume, high complexity, Dialogflow interfaces over text message. So you'll actually see me discuss some of what we've done in that real-world application in this class as a bit of a case study. Anyway, I hope you enjoy the class.
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.