Let's Talk: Introduction to Machine Learning for Text Processing
Limited Seats Available!
Introduction to Machine Learning for Text Processing
Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become ubiquitous in everyday life. From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core.
When you look at a complex website like Facebook, Amazon, or Netflix, it is very likely that every part of the site contains multiple machine learning models. In this talk, we are going to learn the principle of teaching machines to do a certain task by just showing an example of the tasks. The talk will cover the basic theories of machine learning and how to apply machine learning to extract knowledge from a raw text. Finally, a practical demo of using machine learning to do sentiment analysis on movie reviews is presented.
About Your Speaker
Mundher is a scholar, scientist, and engineer in artificial intelligence (AI). Currently, he is doing his PhD in deep learning at Monash University. He has published several research papers in deep learning and machine learning. Besides that, he is a machine learning engineer at Aerohub where he works with other engineers side-by-side to develop intelligent drones. His expertise lies in machine/deep learning and computer vision. He is interested in applying machine learning to different domains to make human life much better, safer and more effective. He spends his free time to democratize AI by spreading the knowledge and help people across social media to solve AI technical problems and making AI-related memes.
- The event description was updated. Diff#391765 2018-11-29 07:01:00