This webinar explores the role of feature engineering in improving machine learning models. It discusses the iterative process of refining the feature set using a dataset and incorporating domain-specific knowledge. Four experiments are presented, demonstrating strategies to enhance model accuracy and reduce error.

Attendees will gain insights into effective approaches for improving predictive model performance through iterative feature engineering. 

- Learn how to apply domain knowledge and experiment with feature types 

- Discover what iterative feature engineering is and why it matters 

- See practical, hands-on use of iterative feature engineering

Local ODSC chapter in NYC, USA

Instructor's Bio

Dr. Joshua Gordon

Senior Data Scientist at dotData

With a PhD in Statistics from the University of California Los Angeles and over 10 years of experience in Statistics, Data Engineering, and Machine Learning, he is an expert in the field. Joshua has published academic articles, taught university courses, and received awards for his innovative work in technology. He is constantly seeking out new information and technologies to improve his skills and stay on top of the latest industry trends. Through his hard work and dedication to his craft, Joshua strives to push the boundaries of what is possible in the world of data science. He believes in the transformative power of education and collaboration, and seeks to share his knowledge and expertise with others.


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