Workshops

Machine Learning Week

May 19 – 28, 2021


Blue circle workshops are for All Levels
Red triangle workshops are Expert/Practitioner Level

All times are Pacific Daylight Time (PDT/UTC-7)

Wednesday, May 19

Big Data: The Leading Ways to Improve Business with Data Science (Non-Technical)


Full-day: 8:00am – 3:00pm PDT
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.

Marc Smith
Marc Smith

Chief Social Scientist

Connected Action Consulting Group

Vladimir Barash
Vladimir Barash

Director

Graphika

Wednesday, May 19

Machine Learning with R: A Hands-On Introduction


Full-day: 7:30am – 3:30pm PDT
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.

Robert Muenchen
Robert Muenchen

Manager of Research Computing Support

Wednesday, May 19

The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques


Full-day: 7:15am – 2:30pm PDT
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

John Elder Ph.D.
John Elder Ph.D.

Founder & Chair

Thursday, May 20

Ensemble Models: Supercharging Machine Learning


Full-day: 8:00am – 3:00pm PDT
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.

Dean Abbott
Dean Abbott

Co-Founder and Chief Data Scientist

Thursday, May 20

Machine Learning with Python: A Hands-On Introduction


Full-day: 8:00am – 3:00pm PDT
Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general.

Clinton Brownley
Clinton Brownley

Data Scientist

Thursday, May 20

Machine Learning Operationalized for Business: Ensuring ML Deployment Delivers Value


Full-day: 8:00am – 3:00pm PDT
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.

Thursday, May 20

Spark on Hadoop for Machine Learning: Hands-On Lab


Full-day: 8:30am – 3:30pm PDT
Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists.

James Casaletto
James Casaletto

PhD Candidate

UC Santa Cruz Genomics Institute and former Senior Solutions Architect, MapR

Friday, May 21

The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them


Full-day: 7:15am – 2:30pm PDT

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

John Elder Ph.D.
John Elder Ph.D.

Founder & Chair