Hands-on Workshop: Feature Engineering Made Simple

✨Join this hands-on workshop to explore new ways of simplifying the feature engineering process with feature optimization and open source✨

Overview:
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