Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Ali Zaidi

Ali Zaidi
Data Scientist, Microsoft

@alikzaidi

Ali Zaidi is a PhD student in statistics at UC Berkeley. Previously, he was a data scientist in Microsoft’s AI and Research Group, where he worked to make distributed computing and machine learning in the cloud easier, more efficient, and more enjoyable for data scientists and developers alike. Before that, Ali was a research associate at NERA (National Economic Research Associates), providing statistical expertise on financial risk, securities valuation, and asset pricing. He studied statistics at the University of Toronto and computer science at Stanford University.

Sessions

11:00am–11:40am Wednesday, March 27, 2019
Building high-performance text classifiers on a limited labeling budget
Data Science, Machine Learning & AI
 Location: 2010
Secondary topics:  AI and Data technologies in the cloud, Text and Language processing and analysis
Robert Horton (Microsoft), Mario Inchiosa (Microsoft), Ali Zaidi (Microsoft)
Average rating: ****.
(4.70, 10 ratings)
Robert Horton, Mario Inchiosa, and Ali Zaidi demonstrate how to use three cutting-edge machine learning techniques—transfer learning from pretrained language models, active learning to make more effective use of a limited labeling budget, and hyperparameter tuning to maximize model performance—to up your modeling game. Read more.