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

Machine Learning Infrastructure Engineer @Netflix Ville Tuulos

Human-Centric Machine Learning Infrastructure @Netflix

Machine Learning Research/Engineering Director @Netflix Justin Basilico

Artwork Personalization @Netflix

Senior Manager & Heading AI for Growth and Communication Relevance @LinkedIn Hema Raghavan

Nearline Recommendations for Active Communities @LinkedIn

Senior Researcher in the Quantitative Financial Research Group @Bloomberg Chakri Cherukuri

Jupyter Notebooks: Interactive Visualization Approaches

Tech Lead Fairness, Transparency, Explainability & Privacy Efforts @LinkedIn Krishnaram Kenthapadi

Fairness, Transparency, and Privacy in AI @LinkedIn

Software developer @IBM, committer to Apache Bahir and contributor to Jupyter Enterprise Gateway Christian Kadner

Create a Fair & transparent AI Pipeline with AI Fairness 360


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Justin Basilico
Machine Learning Research/Engineering Director @Netflix Justin Basilico