Machine Learning

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

The name machine learning was coined in 1959 by Arthur Samuel. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

Position on the Adoption Curve

Presentations about Machine Learning

Staff Developer Relations Engineer @Google Cloud Platform Amy Unruh

Introduction to Kubeflow and Kubeflow Pipelines (Morning Session)

Staff Developer Relations Engineer @Google Cloud Platform Amy Unruh

ML/AI Panel

Director of Data Science @DevotedHealth Chris Albon

ML/AI Panel

AI @Stitch Fix June Andrews

ML/AI Panel

Product Manager (TensorFlow) @Google Paige Bailey

ML/AI Panel

Senior Developer Relations Engineer Company @Google Melanie Warrick

ML/AI Panel

Interviews

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Victor Dibia
Research Engineer in Machine Learning @cloudera Victor Dibia
Sasha Rosenbaum
Program Manager on the Azure DevOps Engineering Team @Microsoft Sasha Rosenbaum
Justin Basilico
Machine Learning Research/Engineering Director @Netflix Justin Basilico
Ville Tuulos
Machine Learning Infrastructure Engineer @Netflix Ville Tuulos
Sarah Aerni
Senior Manager, Data Science @Salesforce Sarah Aerni