PELAJARI, SAMBUNGKAN, BANGUN
Microsoft Reactor
Bergabung dengan Microsoft Reactor dan terlibat dengan pengembang secara langsung
Siap untuk mulai menggunakan AI dan teknologi terbaru? Microsoft Reactor menyediakan acara, pelatihan, dan sumber daya komunitas untuk membantu pengembang, pengusaha, dan startup dibangun berdasarkan teknologi AI dan banyak lagi. Gabung dengan kami!
PELAJARI, SAMBUNGKAN, BANGUN
Microsoft Reactor
Bergabung dengan Microsoft Reactor dan terlibat dengan pengembang secara langsung
Siap untuk mulai menggunakan AI dan teknologi terbaru? Microsoft Reactor menyediakan acara, pelatihan, dan sumber daya komunitas untuk membantu pengembang, pengusaha, dan startup dibangun berdasarkan teknologi AI dan banyak lagi. Gabung dengan kami!
Paradoxes in Data Science
1 Desember, 2021 | 5.30 PM - 6.30 PM (UTC) Waktu Universal Terkoordinasi
- Format:
Streaming Langsung
Topik: Ilmu Data &Pembelajaran Mesin
Bahasa: Inggris
What is this session about?
In this talk, Pier Paolo Ippolito will walk you through some of the main paradoxes associated with Data Science and how they can be identified. The session will aim to first define what paradoxes are and then provide some examples of when they might occur when solving Data Science tasks.
Who is it aimed at?
Data Scientists, Machine Learning Engineers, Statisticians, Software Engineers.
Why should people attend?
As part of this session, we will be highlighting some of the most common paradoxes associated with Data Science and its statistical foundations
Any Prerequisites?
Statistics, Programming, Mathematics
Speaker Name:
Pier Paolo Ippolito
Pier Paolo Ippolito is a Data Scientist at SAS and MSc in Artificial Intelligence graduate with an interest in research areas such as Data Analytics, Machine Learning, and Cloud Development. Aside from his work activities, he is a freelancer and technical writer for Towards Data Science. LinkedIn: https://www.linkedin.com/in/pierpaolo28/
Web Contacts: https://linktr.ee/pierpaolo28
Learn Module
https://docs.microsoft.com/en-us/learn/paths/understand-machine-learning/