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AI on Edge, encounter between Data Science and IoT Jean-Pierre Riehl
That session explains how to put AI algorithms on Edge devices, with feedbacks from a computer vision project
The video is not available to view online.
Art of Feature Engineering- For Machine Learning Sandip Pani
The most challenging area of machine learning are Data acquisition, Feature extraction, Feature Selection. Almost in all data science project, 80% of time people spend in Data acquisition and Feature engineering.
Rapid Requirements: Introducing the Machine Learning Canvas Terry McCann
In this session, we will introduce the Advancing Analytics Machine Learning Canvas and how it can be used to capture requirements for Machine Learning Projects.
Taking Models to the Next Level with Azure Machine Learning Amy Boyd
Learn how Tailwind Traders data science team uses Azure Machine Learning features and services to create bespoke open source NLP models and optimise them. Includes Automated ML, Azure ML SDK and Hyperparameter tuning
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