Machine Learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, 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 or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Source: https://en.wikipedia.org/wiki/Machine_learning

Position on the Adoption Curve

Presentations about Machine Learning

Chief Data Scientist and CEO @QuantUniversity Sri Krishnamurthy

Machine Learning & AI: Core Techniques Workshop

Leading the Machine Learning Engineering Work for Time Predictions @UberEats Zi Wang

Time Predictions in Uber Eats

Engineering Manager @Facebook AI Jeff Smith

From Research to Production With PyTorch

Interviews

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