Tech Stack : Application,Node.js, Artificial Intelligence, IBM Watson , Cloud
Built for developers familiar with JavaScript and Node.js interested in making a web UI chatbot with artificial intelligence abilities, this code pattern uses the IBM Watson Node.js SDK to include conversation interaction, anger detection, natural language understanding, and answer discovery. Answers are discovered from a collection of FAQ documents. Built as a fictional financial institution, this app calls out to simple banking services code as an example of how to include external business data in a conversation response.
OPTION 1 : Project
Industry Mentor from CEW will be assigned to help on the project.
Project Lifecycle will be : Scope, Architecture & Planning, Design, Coding, Testing, Go-Live/Award
Technology Involved : Application,Node.js, Artificial Intelligence, IBM Watson , Cloud , Agile, Functional & Non Functional Requirements Capturing, Architecture & Solution Design, Project Plan, Project Estimatation, Use Case Modelling, UML Design, Process Flow Diagrams, UX Personas, Stakeholder Analysis, UX Best Practices, Responsive Design, Coding Best Practices, Unit Testing, Github, Deployment of Project, Devops, Automation, Go Live Procedures + this project
(limited time offer)
OPTION 2 : Class & Project
Industry Expert Teachers from CEW will be assigned for 12 weeks coaching on the technologies used in project.
After 12 weeks of teaching, project work will start.
Industry Mentor from CEW will be assigned to help on the project.
Project Lifecycle will be : Scope, Architecture & Planning, Design, Coding, Testing, Go-Live/Award
Technology Involved : Application,Node.js, Artificial Intelligence, IBM Watson , Cloud , Agile, Functional & Non Functional Requirements Capturing, Architecture & Solution Design, Project Plan, Project Estimatation, Use Case Modelling, UML Design, Process Flow Diagrams, UX Personas, Stakeholder Analysis, UX Best Practices, Responsive Design, Coding Best Practices, Unit Testing, Github, Deployment of Project, Devops, Automation, Go Live Procedures + this project
(limited time offer)
No Other Class in the world teaches you Real life implementation | Agile Implementation | Requirements Capturing | Architecture & Solution Design | Project Plan | Project Estimatation | Use Case Modelling | UML Design | Process Flow Diagrams | UX Personas | Stakeholder Analysis | UX Best Practices | Responsive Design | Coding Best Practices | Unit Testing | Github | Deployment of Project | Devops | Automation |Go Live Procedures + this project of the projects like we do
What you will learn ?
- Real-world how IT projects are implemented
- Implement project using Application,Node.js, Artificial Intelligence, IBM Watson , Cloud
- Capture Requirements of the project using Use Case Modelling (Stakeholders, Personas, Main Scanario, Alternate, Negative, Edge Cases)
- Define Functional & Non-Functional Use Cases
- Create project design using UML Modelling
- Implement project coding using code respositories.
- How Google Analytics, Search Engine Optimization(SEO) are implemented.
- How UX Banners are created.
- Testing using unit tests(create & execute)
- Deployment of the project in cloud
- User Acceptance Testing - How client identify issues, how you fix issues
- Go Live of the project
Description
In this pattern, you create a chatbot using Node.js and IBM Watson Assistant. The flow is enhanced by using Watson Natural Language Understanding to identify entities and Watson Tone Analyzer to detect customer emotions. For FAQs, a call to the Watson Discovery service uses passage retrieval to pull answers from a collection of documents.
When you complete this pattern, you will understand how to:
1. Create a chatbot that converses through a web UI using Watson Assistant and Node.js
2. Use Watson Discovery with passage retrieval to find answers in FAQ documents
3. Use Watson Tone Analyzer to detect emotion in a conversation
4. Identify entities with Watson Natural Language Understanding
FLOW
1. The FAQ documents are added to the Watson Discovery collection.
2. The user interacts with a chatbot through the app UI.
3. User input is processed with Tone Analyzer to detect anger. An anger score is added to the context.
4. User input is processed with Natural Language Understanding. The context is enriched with detected entities and keywords (for example, a location).
5. The input and enriched context is sent to Watson Assistant, which recognizes intent, entities, and dialog paths. It responds with a reply, an action, or both.
6. Optionally, a requested action is performed by the app. This action might include looking up additional information from bank services to append to the reply or using Discovery to reply with an answer from the FAQ documents.
Timelines : 8 weeks
Project will go through the phases of scope, design, coding, unit testing, UAT, Award(Go Live)
Winner will be chosen for each phase(scope,design,coding,unit testing,UAT,Go-Live) of the project, cash prize from CEW ′, certificate, cloud credits will be provided for each phase.
