Mar
30
6:00pm
Hands-on Workshop: Feature Engineering Made Simple
By Data Science Connect
More art than science, Feature Engineering consumes 70-80% of the machine learning workflow. It is ad-hoc, messy, error-prone, and has an outsize influence on the quality and resilience of predictive models.
Join us for a hands-on workshop that explores new ways of refining feature engineering, turning it into a systematic and procedural process, way more efficient than how it occurs currently.
In this workshop, participants will perform a hands-on, end-to-end, model building workflow, with particular emphasis on feature engineering using Anovos, an open source library that supports data ingestion, cleansing, analytics, feature generation and transformation.
Audience:
This workshop is for practitioners with knowledge of machine learning, data engineering or data science and who have basic Python skills.
Topics Covered:
- Feature engineering
- Machine learning
- Data cleansing and analysis
- Data drift and stability
- The cold start problem in initial feature selection
- Feature generation using transformation
- Hands on use of open source library: Anovos
- Predictive modeling
- Implementation of a real world use case
Takeaways:
- Why feature engineering is so difficult and strategies to make it more procedural
- The importance of understanding data drift and stability
- The complexity of initial feature selection and how to address it
- How to use open source library, Anovos, to ingest, cleanse and analyze your data
- How to use open source library, Anovos, to generate optimal features for your modeling
- How to optimize the features you are selecting to build resilient, high performing models
*Instructor to be announced soon!
hosted by
Data Science Connect
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