As one of the seven patterns of AI, Personalization & Recommendation Systems allow the system to develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes, including displaying relevant content, recommending relevant products, providing personalized recommendations and guidance, advice, and feedback. This course provides an advanced AI concept overview of hyperpersonalization systems, which algorithms are commonly used for this pattern, and use case examples. This course is geared towards a more technical audience.
Foundations of Personalization & Recommendation Systems
This course is currently closed
What Will I Learn?
- What is personalization and recommendation?
- How have recommendation and personalization systems been built without AI?
- AI applied to personalization and recommendation: Hyperpersonalization
- AI-based personalization applications and use cases
- AI-based recommendation applications and use cases
- AI approaches for hyperpersonalization systems
- Data collection and preparation needs and challenges for hyperpersonalization systems
- AI technology approaches for hyperpersonalization
- Determining ROI of autonomous systems
- Privacy and ethics considerations for AI-powered personalization and recommendation systems
Who is this Training For?
- Computer Systems Programmer
- Artificial Intelligence Research Associate
- Data Scientist
- AI / Machine Learning (ML) Engineer
- AI Assurance Engineer
- Test & Evaluation Engineer, System Engineer
- Network Analyst
- Data Analyst
- Operations Research Analyst
- Deployment Engineer
- Knowledge Operations Manager
- Network Infrastructure Engineer
- Information Technician