ThisrepositorycontainstheproblemsetsaswellasthesolutionsfortheStanfordCS229-MachineLearningcourseonCourserawritteninPython3.Someadditionalnotestakenbymearealsoincluded.
PleasecheckoutthecoursewebsiteandtheCourseracourse.
Pleasenotethatyoursolutionswon'tbegradedandarenotaffiliatedtoCourserainanyway.Ifyouranswersdifferfrommineandyouarguethatyoursarebetter,pleasecreateanissueonGitHub.
InstallationMakesureyouhavejupyternotebooksinstalled.Youcanfindinstructionshere.
ThefollowingPythonpackagesareused:
NumpyScipyMatplotlibPandasPillowNaturalLanguageToolkitYoucaninstallalldependenciesusing:
python3-mpipinstall-rrequirements.txtInstructionsPleasedownloadtheexercises(pdf)fromtheCourseracourse.SomeinstructionsareincludedintheNotebooks.CompletetheexercisesintheexercisesNotebook.CompareyouranswerstothecodeinsolutionsNotebook.ContentsLinearRegressionLogisticRegression&RegularizationMulticlassClassifcation&NeuralNetworksNeueralNetworksLearningRegularizedLinearRegressionandBiasv.s.VarianceSupportVectorMachinesK-meansClusteringandPrincipalComponentAnalysisAnomalyDetectionandRecommenderSystemsCopyrightNoticeAllcode,exercises,dataandotherfilesinthisrepoare©StanfordUniversity.IfyouareunhappyaboutmehostingthesefilesonGitHubforeducationalpurposes,pleasesendmeanemail.
Thecodewas'translated'toPythonbyRickWierenga.SomeoftheinstructionsaremodifiedtobetterfitthePythonecosystembymetoo.Thedata,backgroundinformationandtheintendedexercisearethesame.
©2020RickWierenga
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