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dc.contributor.author Langmore, Ian
dc.contributor.author Krasner, Daniel
dc.date.accessioned 2023-07-01T01:22:45Z
dc.date.available 2023-07-01T01:22:45Z
dc.date.issued 2013
dc.identifier.uri ${sadil.baseUrl}/handle/123456789/3732
dc.description 141 p. (PDF) sm
dc.description.abstract This book focuses more on the statistics end of things, while also getting readers going on (basic) programming & command line skills. It doesn’t, however, really go into much of the stuff you would expect to see from the machine learning end of things. sm
dc.language.iso en sm
dc.publisher Columbia University sm
dc.subject Programming prerequisites sm
dc.subject History and culture sm
dc.subject The shell sm
dc.subject Streams sm
dc.subject Standard streams sm
dc.subject Pipes sm
dc.subject Philosophy sm
dc.subject Version control with git sm
dc.subject Online materials sm
dc.subject Basic git concept sm
dc.subject Common git workflows sm
dc.subject Linear move from working to remote sm
dc.subject Merge conflicts sm
dc.subject Simple shell scripts sm
dc.subject Template for a python CLI utility sm
dc.subject Notation sm
dc.subject Linear regression sm
dc.subject Logistic regression sm
dc.subject Models behaving well sm
dc.subject Text data sm
dc.subject Processing text sm
dc.subject Python RE module sm
dc.subject The python NLTK library sm
dc.subject Naive bayes sm
dc.title Applied Data Science sm
dc.type Book sm


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