21 Jan 2022 (Fri) | 12:00 - 18:40 | Webinar
In artificial intelligence, natural language processing (NLP) is one of the hottest topics allowing computers to understand human languages. It has been demonstrated that NLP techniques are continuously evolving and that applications are increasingly being implemented by organizations to solve a wide range of problems. Especially its application and cross utilize with product recommendation and data forecasting.
In this webinar, we aim to equip ICT professionals with the knowledge on understanding the technologies, use cases, trends and applications of NLP technologies, data forecasting & product recommendation.
To equip Hong Kong ICT professionals with knowledge on the latest digital transformation trends and implementation strategies.
Date January 21 2022 (Friday)
Time 12:00 - 18:40
Admission Fee Free
For ICT professionals only
12:00 – 12:10 Registration
12:10 – 12:25 HKEBA Opening - Introduction to PASS, project details & HKEBA
12:25 – 13:45 Workshop Part 1, Q&A and Networking session / Breakout session
13:45 – 13:55 Break
13:55 – 15:15 Workshop Part 2, Q&A and Networking session / Breakout session
15:15 – 15:25 Break
15:25 – 16:45 Workshop Part 3, Q&A and Networking session / Breakout session
16:45 – 16:55 Break
16:55 – 18:20 Workshop Part 4, Q&A and Networking session / Breakout session
18:20 – 18:25 HKEBA Summary - How it may be relevant to attendees
18:25 – 18:40 Closing & Feedback Survey
*Please note that questions will only be taken during the Q&A session. And hands raised during webinar will only be attended for technical difficulties.
Ming Cheung - Research Scientist, The Lane Crawford Joyce Group
Sunny Wong - Director of Product Development, Chatbot.com.hk / Set Sail Software
Wai Hei Chow - Solutions Architect, Amazon AWS
Terence Pong - Vice President, Product Marketing, Fano Labs
The above speakers have: 1. At least 3 years of the topics experience, or 2. Currently work for/worked with or working closely with the topics.
Introduction to topics / technologies
Application of Machine Learning: Data Forecasting, Product Recommendation & NLP
Retail Case Sharing in Machine application
Process of running Machine Learning Problem
Framing problem - How to Define the Machine Learning problem and propose a solution
Data Collection, Construction and Cleaning - Transform all data for the purpose of Machine Learning Model
Train and select the Machine Learning Model
Machine Learning feedback loop for improving the accuracy of data forecasting
Real Demo - Example of how Product Recommendation is used in eCommerce and Retails
Ethical issue behind the Machines Learning
Note: The seminar videos and materials will be uploaded onto HKEBA’s website for wider dissemination.
Acknowledgment of Support and Disclaimer
This material/event is funded by the Professional Services Advancement Support Scheme of the Government of the Hong Kong Special Administrative Region. Any opinions, findings, conclusions or recommendations expressed in this material/any event organised under this project do not reflect the views of the Government of the Hong Kong Special Administrative Region or the Vetting Committee of the Professional Services Advancement Support Scheme.