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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.
A Heart to Heart with your Data: Emotional Intelligence in A Matt How
Customers have feelings and by harnessing the power of deep neural networks, we can derive emotional insight from their data and use this to improve. Attendees will leave understanding how to connect different types of data to cognitive services.
The video is not available to view online.
Azure Machine Learning: Applying Software Engineering to ML Amy Boyd
Azure Machine Learning is a platform for developing and deploying your machine learning models on Azure. We will look at the life cycle of ML projects: from data, to model, to consumption. This will include Automated Machine Learning capabilities.
Deploy ML models faster with Data Science DevOps Terry McCann
In this session I will show you how to apply DevOps practices to speed up your development cycle and ensure that you have robust deployable models. We will focus on the Azure cloud platform in particular, however this is applicable to other platforms
Python Pipeline Primer: Data Engineering with DataBricks Simon Whiteley
Azure DataBricks is a PaaS offering of Apache Spark, which allows for blazing fast data processing! How can data engineers harness the in-memory processing power? Azure DataBricks can be your data ingestion, transformation and curation tool of choice
The video is not available to view online.
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