English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 11h 53m | 3.08 GB
Making your applications smart by combining Cognitive and AI capabilities
IBM Watson has evolved from being a game show winning question & answering computer system to a set of enterprise-grade artificial intelligence (AI) application program interfaces (API) available on IBM Cloud. These Watson API’s can ingest, understand & analyze all forms of data, allow for natural forms of interactions with people, learn, reason – all at a scale that allows for business processes and applications to be reimagined.
This course will give you a hands-on introduction to getting a detailed understanding of the Watson AI API’s, how to train them and eventually build applications using them. You will go through the fundamentals behind each of the API’s, lots of code examples on how to use them on different types of unstructured data, spot the scenarios where you can apply them as well as real-life use case examples. You will learn about how to build conversational apps a.k.a., chat-bots, analyze text at a deeper level, transcribe audio, training a machine to classify & detect objects in pictures, extract entities, emotions, sentiment and relationships from news articles etc.
You will also learn the different types of data basics of AI including machine & deep learning, approach to building AI systems. You will learn about the basics of getting started with IBM Cloud, Watson and setting up an environment to build AI infused apps.
By the end of the course, you will have a complete understanding of the various Watson API’s and will have developed the skills to effectively use them in applications and business processes you may be working on.
This practical course is packed with step-by-step instructions, working examples, and helpful advice on getting started with IBM Watson. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.
What You Will Learn
- Learn the fundamentals of IBM Cloud and creating service instances
- Understand the various IBM Watson AI API’s in detail, how to train and when to use them
- Learn Watson Assistant to build an IT Support Assistant conversational application
- Apply Watson Natural Language Understanding to build an Customer Complaints Analyzer
- Train Watson Speech to Text to build a financial earnings call analyzer & enricher application
- Train Watson Visual Recognition to classify & detect rooms in a home
- Become proficient in using the Watson API’s to build applications in Python, Node-RED and Node.js
- Best practices when using each of the API’s
Table of Contents
Introducing the IBM Watson Platform
1 The Course Overview
2 Fundamentals
3 Fundamentals – Part 2
4 Introducing IBM Watson
5 The IBM Watson Platform
6 Adapting Watson
7 Examples
Getting Started with coding for IBM Watson
8 Watson API’s
9 IBM Cloud
10 Development Environment
11 Hello Watson
12 Hello Watson (Continued)
13 IBM Node-RED
14 IBM Node-RED (Continued)
15 Python and Node.js SDK
16 Python and Node.js SDK (Continued)
Build Conversational Systems Using the Watson Conversation Service
17 Watson Assistant in Depth
18 Watson Assistant in Depth (continued)
19 Define Intents and Entities Workspace
20 Define Intents
21 Define Entities
22 Build Dialog Overview
23 Build Dialog Conditions and Responses
24 Build Dialog Context, Slots and Folders
25 Build Dialog Advanced Responses and APIs
26 Evaluate and Deploy the Model
27 Build – IT Support Assistant
28 Improving Models Continuously
29 Applying the Capability in Various Use Cases
Rich Text Analytics Using Watson Natural Language Understanding for Your Application
30 Watson NLU in Depth
31 Watson NLU in Depth – Part 2
32 Understand Entities and Relations
33 Concepts, Categories, and Keywords
34 Sentiment and Document Emotion
35 Build – Analyzing Customer Complaints
36 Build – Analyzing Customer Complaints – Part 2
37 Applying NLU in Various Use Cases
Adding Speech Recognition Capabilities to Your Apps Using Watson Speech to Text
38 Watson Speech to Text in Depth
39 Watson Speech to Text in Depth (Continued)
40 Key Concepts
41 Key Concepts (Continued)
42 Testing Watson Speech To Text Model
43 Improving STT Model Using Custom Words
44 Improving STT Model Using Custom Words(continued)
45 Build Your Own Custom Acoustic Model
46 Build – Company Earnings Call Transcript Application
47 Applying the Capability in Various Use Cases
Train Image Recognition Models Using Watson Visual Recognition
48 Watson Visual Recognition in Depth
49 Watson Visual Recognition in Depth (Continued)
50 Classifying Images
51 Classifying Images (Continued)
52 Detecting Food and Faces
53 Extracting Text from Images
54 Introduction to Watson Studio
55 Overall Approach to Training
56 Training the Classifier
57 Invoke Model, Best Practices and Applicable Cases
58 Apply the Capability in Various Use Cases and Convert to Core ML
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