Overview
Course Outline
Review of Cognitive Concepts
Review general concepts behind the term cognition
Explore the main characteristics of a cognitive system
Understand why it matters to build cognitive systems
Structure of a Cognitive System
Understand the different computing paradigms for solving problems
Change the focus from rules definition and development to data analysis and training
Explore the conceptual components that make a cognitive system
Watson – Your Next AI Platform
Go through a brief history of Watson and the Jeopardy challenge
Review the evolution of Watson after it won the Jeopardy contest
Review the current status of Watson and the way of using it
Review of Watson APIs
Explore some high level categorization of API functionality
Look at the summary of natural language and empathy APIs
Review some demos of the APIs
Explore signal processing APIs
Review data analysis services
Explore some demos of the remainder APIs
Watson Assistant Training
Understand the natural language processing capability and the difference with traditional approaches
Explore the high level structure of a conversation
Define an intent as a conversation building block
Complement the intent detection with the entities parsing
Model the script of the dialog flow
Put all the pieces together in a sample
Introduction to Discovery Service
Explore the document processing capabilities offered in Watson
Compare cognitive search and analytics
Look at a practical use of Discovery for enhancing chatbot behaviour
Build Your Own Chatbot
Define the set of intents and entities
Create your workspace and teach Watson your utterances examples
Model the dialog flow and try the solution
Natural Language Understanding
Understand metadata and its role in natural language processing
Explore the kind of metadata that Watson can extract from your data
Use a sample app for looking at the results with feature extraction with NLU
Enrichments in Discovery Service
Review the three stages for processing a set of documents
Understand the structure of the Discovery Service and the functionalities it offers
Configure your own environment for uploading documents and doing the NLP processing
Find Insights from Unstructured Data
Understand the two types of queries you can use
Explore the Discovery Query Language for querying metadata
Use the GUI for building your own queries and finding insights
Understanding Visual Recognition
Understand the kind of information that Watson extracts from images
Explore the concepts of model and classes
Review the high-level features that you can use in your solutions
Standard Model
Explore the tags returned by the standard general model
Look at the capabilities of face detection
Review the beta models and create your own service and classify images
Creating Custom Models
Design the classification taxonomy
Understand the training method and the concepts of positive and negative examples
Review some useful tips for building classifiers and train your own service
Introducing IBM Watson
The IBM Watson Platform
Adapting Watson
Watson API’s
IBM Cloud
Development Environment
Hello Watson
IBM Node-RED
Python and Node.js SDK
Watson Assistant in Depth
Define Intents and Entities Workspace
Define Intents
Define Entities
Build Dialog
Build Dialog Overview
Build Dialog Conditions and Responses
Build Dialog Context, Slots and Folders
Build Dialog Advanced Responses and APIs
Evaluate and Deploy the Model
Build: IT Support Assistant
Improving Models Continuously
Applying the Capability in Various Use Cases
Watson NLU in Depth
Understand Entities and Relations
Concepts, Categories, and Keywords
Sentiment and Document Emotion
Build: Analysing Customer Complaints
Applying NLU in Various Use Cases
Watson Speech to Text in Depth
Testing Watson Speech to Text Model
Improving STT Model Using Custom Words
Build Your Own Custom Acoustic Model
Build: Company Earnings Call Transcript Application
Watson Visual Recognition in Depth
Classifying Images
Detecting Food and Faces
Extracting Text from Images
Introduction to Watson Studio
Overall Approach to Training
Training the Classifier
Invoke Model, Best Practices and Applicable Cases
Apply the Capability in Various Use Cases and Convert to Core ML
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