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by Josh Zheng, Tom Markiewicz
Getting Started with Artificial Intelligence
1. Introduction to Artificial Intelligence
The Market for Artificial Intelligence
Avoiding an AI Winter
Artificial Intelligence, Defined?
Artificial Intelligence
Machine Learning
Deep Learning
Applications in the Enterprise
Next Steps
2. Natural Language Processing
Overview of NLP
The Components of NLP
Entities
Relations
Concepts
Keywords
Semantic Roles
Categories
Emotion
Sentiment
Enterprise Applications of NLP
Social Media Analysis
Customer Support
Business Intelligence
Content Marketing and Recommendation
Additional Topics
How to Use NLP
Training Models
Challenges of NLP
Summary
3. Chatbots
What Is a Chatbot?
The Rise of Chatbots
Natural Language Processing in the Cloud
Proliferation of Messaging Platforms
Natural Language Interface
How to Build a Chatbot
The Messaging Channel
The Backend
Challenges of Building a Successful Chatbot
Best Practices
Tip #1: Introduce Your Chatbot to First-Time Users
Tip #2: Add Variations to Your Responses
Tip #3: Make a Main Menu That’s Accessible Anywhere
Tip #4: Have Context Awareness
Tip #5: Be Able to Fix Incorrect Inputs
Tip #6: Handle the “I Do Not Understand” Case
Tip #7: Be Careful About Creating a Personality
Industry Case Studies
Autodesk: Customer Support
Staples: Conversational Commerce
Summary
4. Computer Vision
Capabilities of Computer Vision for the Enterprise
Image Classification and Tagging
Object Localization
Custom Classifiers
How to Use Computer Vision
Computer Vision on Mobile Devices
Best Practices
Quality Training Images
Use Cases
Satellite Imaging
Video Search in Surveillance and Entertainment
Additional Examples: Social Media and Insurance
Existing Challenges in Computer Vision
Implementing a Computer Vision Solution
Summary
5. AI Data Pipeline
Preparing for a Data Pipeline
Sourcing Big Data
Storage: Apache Hadoop
Hadoop as a Data Lake
Discovery: Apache Spark
Spark Versus MapReduce
Machine Learning with Spark
Summary
6. Looking Forward
What Makes Enterprises Unique?
Current Challenges, Trends, and Opportunities
Data Confinement
Little Data
Inaccessible Data Formats
Challenges of Ensuring Fairness, Accountability, and Interpretability
Scalability
Social Implications
Summary
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