Until her passing in March 2019, Dr. Hui Li was a Principal Staff Scientist of Data Science Technologies at SAS. Dr. Li’s most memorable contribution on this blog is her guide to machine language algorithms, which continues to be referenced by millions of data science enthusiasts around the world. Dr. Li's work focused on deep learning, cognitive computing and SAS recommendation systems in SAS Viya. She received her PhD degree and Master’s degree in Electrical and Computer Engineering from Duke University. Before joining SAS, she worked at Duke University as a research scientist and at Signal Innovation Group, Inc. as a research engineer. Her research interests included machine learning for big, heterogeneous data, collaborative filtering recommendations, Bayesian statistical modeling and reinforcement learning.
Este recurso está diseñado principalmente para científicos de datos, analistas principiantes o intermedios que están interesados en identificar y aplicar algoritmos de machine learning para abordar los problemas de su interés. Una pregunta típica de un principiante, cuando se enfrenta a una gran variedad de algoritmos de machine learning, es:
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should
When building models, data scientists and statisticians often talk about penalty, regularization and shrinkage. What do these terms mean and why are they important? According to Wikipedia, regularization "refers to a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information usually
