Machine Learning Resource Guide
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MachineLearningMastery
Web: http://MachineLearningMastery.com
Email: jason@MachineLearningMastery.com
MachineLearningResourceGuide
byJasonBrownlee,PhD
Copyright©2014JasonBrownlee,AllRightsReserved.
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Table of Contents
Introduction
Books
BeginnerBooks
PracticalBooks
PythonBooks
RBooks
Textbooks
Communities
StackExchange
Reddit
Quora
Other
Videos
UniversityCourses
PaidCourses
OtherVideos
UniversityCourseMaterial
UndergraduateLevel
GraduateLevel
SoftwareandLibraries
Competitions
Guides
Beginner
Novice
Intermediate
JetFuel
ConnectWithMe!
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Introduction
Hithere,mynameisJasonfromMachineLearningMastery.com.Thanksfordownloadingthis
MachineLearningResourceGuide.
Ihaveworkedhardtocollectandlistonlythebestresourcesthatwillhelpyoujumpstartyour
journeytowardsmachinelearningmastery.I’vecategorizedtheresourcesintomainthemes
suchasvideos,booksandcourses.
I’mcertainyouwillfindgreatvalueintheresourceslistedinthisguide.Takeyourtimeandselect
amediumorresourcetypeyoupreferandstartworkingthroughresourcesonebyone.Trynot
tooverloadyourself.Remembertothinkhardaboutwhatyouwantfromaresourceandactively
takenotes.
I’minterestedtohearwhatresourcesyoutry,sendmeanemailandletmeknowvia
jason@MachineLearningMastery.comorvisitmysiteMachineLearningMastery.comandleavea
comment.Ihopetohearfromyousoon.
JasonBrownlee.
4/15 http://MachineLearningMastery.com
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Books
Ireadalotofbooks,andeveninthisageofebooks,Ilikehavingalotofreferencebooksonthe
bookshelf.IalsolikehavingbooksinPDFformsoIcansearchthemquicklyandpulloutthe
informationIneed.
Thebookslistedinthissectionaregroupedbyafewdifferentcriteriathatyoumayfinduseful,as
suchyoumayseeafewduplicatesacrossthedifferentlistsofbooks.
IhaveprovidedlinkstoeachbookonAmazon.ThelinksareaffiliatelinkswhichmeansIwillget
afewcentsfromAmazonifyoudecidetobuyabook.
Myadvice:Pickonebookandreadit,covertocover.
Beginner Books
Thesearebooksfortheabsolutebeginnertogetafeelingforwhatmachinelearningorworking
withdataisallabout.Fromabusinessandsemitechnicalperspective.
●PredictiveAnalytics:ThePowertoPredictWhoWillClick,Buy,Lie,orDie
●DataScienceforBusiness:Whatyouneedtoknowaboutdatamininganddataanalytic
thinking
●DataSmart:UsingDataSciencetoTransformInformationintoInsight
Practical Books
Ifyouareaprogrammerorengineerandarelookingforabookwithcodeexamplestoimplement
orexecute,thesearebooksforyou:
●ProgrammingCollectiveIntelligence:BuildingSmartWeb2.0Applications
●DataMining:PracticalMachineLearningToolsandTechniques
●MachineLearningforHackers
●MachineLearning:AnAlgorithmicPerspective
●MachineLearninginAction
●AppliedPredictiveModeling
Youcanlearnmoreaboutthesebooksinmyblogpost6PracticalBooksforBeginningMachine
Learning
Python Books
Thesearebooksforlearningandapplyingmachinelearningifyouareapythonprogrammer.
●BuildingMachineLearningSystemswithPython
●Learningscikitlearn:MachineLearninginPython
●MachineLearninginAction
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●ProgrammingCollectiveIntelligence:BuildingSmartWeb2.0Applications
●MachineLearning:AnAlgorithmicPerspective
●MiningtheSocialWeb:DataMiningFacebook,Twitter,LinkedIn,Google+,GitHub,and
More
●NaturalLanguageProcessingwithPython
●ProgrammingComputerVisionwithPython:Toolsandalgorithmsforanalyzingimages
●PythonforDataAnalysis:DataWranglingwithPandas,NumPy,andIPython
Youcanlearnmoreaboutthesebooksinmyblogpost:PythonMachineLearningBooks
R Books
IfyouareanRprogrammerorarelookingatapplyingmachinelearninginR,thesebooksarefor
you.
●AppliedPredictiveModeling
●AnIntroductiontoStatisticalLearning:withApplicationsinR
●PracticalDataSciencewithR
●MachineLearningwithR
●DataMiningwithR:LearningwithCaseStudies
●DataMiningandBusinessAnalyticswithR
●DataMiningwithRattleandR:TheArtofExcavatingDataforKnowledgeDiscovery
Youcanlearnmoreaboutthesebooksinmyblogpost:BooksforMachineLearningwithR
Textbooks
Thesearebooksformachinelearningpractitionerslookingtogobeyondthepracticalbooksand
deeperintotheory.Thesearetextbookscommonlyusedinundergraduateandpostgraduate
universitycourses.
●MachineLearning,byTomMitchell
●LearningFromData,byYaserAbuMostafa,MalikMagdonIsmailandHsuanTienLin
●MachineLearning:AProbabilisticPerspective,byKevinMurphy
●PatternRecognitionandMachineLearning,byChristopherBishop
●TheElementsofStatisticalLearning:DataMining,Inference,andPrediction,byTrevor
Hastie,RobertTibshiraniandJeromeFriedman
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Communities
Youwillhavealotofquestionsalongyourjourneytowardmachinelearningmasteryandthere
areexcellentplaceswheremachinelearningexpertscananswerthosequestionsforyou,ifyou
knowwheretolook.
Eachsitelistedbelowallowsyoutocreateanaccountforfreeandaskyourquestion.Review
thetypesofquestionsandanswersofferedineachcommunitybeforeselectingtheright
communityforyoutoaskyourquestion.
Itisverylikelyyourquestionhasbeenaskedandansweredbefore.Trysearchingforitoneach
communitybeforeposting.
Stack Exchange
Thestackexchangesitesarequestionandanswercommunities,sotheyaretargetedtowards
problemsolving.Youcanpostthespecificquestionsyouhave,answerquestionstowhichyou
knowtheanswerand(myfavorite)readquestionsandanswerstodiscovernewmethodsand
perspectives.
TherearefoursitesIliketodipinto:
●CrossValidated:Thissiteisusefulforlowlevelquestionsonalgorithmsandstatistical
methods.
●QuantitativeFinance:(specificallythemachinelearningtag)Thissiteisusefulifyouare
operatinginthefinancialdomain,butgenerallyifyouareworkingwithtimeseriesdata.
●Programmers:(specificallythemachinelearningtag)Greatforspecificcodequestions,
suchasaproblemwithagivenlibraryortoolyouareusing.
●StackOverflow:(specificallythemachinelearningtag)Again,likeprogrammers,greatfor
specificquestionswiththeimplementationsideofmachinelearning.It’salsotheoldest
siteandcancovermachinelearningalgorithmsandlibraries.
Thereisanewsitethathasstartedup,butisstillinbeta,soitmaynotsurvive.ItiscalledData
ScienceandIamfindingitveryinterestingforthegeneralconcernsofappliedmachinelearning
(mixofcodeandmath).
Reddit
Redditisacommunityofcommunitiescalledsubreddits.Agivensubredditcanbequestionand
answersite,alinksharingsiteor(moretypically)amixofthetwo.
AfewsubredditsIfrequentinclude:
●MachineLearning:Containsofmixof“howdoIgetstarted”andmoreadvancedlinksto
machinelearningblogposts.Alsogoodforlinkingtoyourownprojectstogetsome
feedback.
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●ComputerVision:Mostlyquestionsoncomputervisionquestionsboththeoreticaland
practical(suchaslibraries).
●NaturalLanguage:Focusonnaturallanguageprocessing,providingagoodmixof
questionsandlinkstorelevantarticlesandblogposts.
●Statistics:Discussiononstatisticalsoftwareandmethods,greatfordiggingdeeperintoa
givenmethodoralgorithm.
●DataScience:Mostlylinkstopoststhatstraddledataanalysisandmachinelearning.
●BigData:Focusedpostsanddiscussionsonthebigdataecosystem.
Thereareothersubredditsonrelevantandrelatedtopics,butIhavenotfoundthemasuseful.
Quora
Quoraisaquestionandanswersitethatisdividedintotopics,muchlikeredditbutonly
questionsandanswers.Thequestionsaretypicallygoodandtheanswershighquality.Unlike
thestackexchangesites,theyaretypicallylesstechnical,lessproblemfocusedandmore
meaty.
AfewQuoratopicsIfrequentinclude:
●MachineLearning:Usefulforhighlevelquestionsonalgorithms,processes,resources
andgettingstarted.Agoodmix.
●Statistics:Focusondeeperstatisticalmethodsandalgorithms,butincludesalotof
machinelearningcontent.
●DataMining:Goodquestionswithafocusontheappliedsideofmachinelearning,buta
lotofoverlapwithMachineLearning.
●DataScience:MuchliketheDataMiningandMachinelearningtopics,thequestionsare
typicallyahigherlevel.
Therearemanyothertopicsthatmightbeuseful,notlimitedtoDataAnalysis,Predictive
Analytics,NLPandComputerVision.AlsotherearetopicsonspecificmethodssuchasSVM,
DeepLearning,Classification,andR.
Other
TherearesomeothergreatcommunitiesaroundthatIcouldnotclassifyaseasily.
●MetaOptimizeQ+A:LikeCrossValidated,thisisaquestionandanswersitethatisgreat
forlowerlevelquestionsonspecificalgorithmsandmethods.Mathsandtheoryheavy.
●KaggleForums:Greatfordiscussionaroundspecificcompetitionsanddatasets,andfull
ofgreatnuggetsofadviceforfeatureengineering,ensemblingandrefiningyourtest
harnesses.
●DataTau:Asocialnewssitewithafocusonlinkstopostsondataandmachinelearning
relatetopics.Lowtrafficandusefullinks.
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Videos
Videosareagreatwaytolearnaboutmachinelearning,bothforlectureandtutorialcontent.
University Courses
ThereareuniversitycoursesthatareofferedonlineforfreebyorganizationssuchasCoursera
andedX.Theyincludevideolectures,homeworkthatisassessed,quizzesandtests.Someof
thecoursesalsohavejustthevideolectureslistedonsiteslikeYouTube.Trysearching.
●Stanford:MachineLearning,byAndrewNg
●Stanford:ProbabilisticGraphicalModels,byDaphneKoller
●Caltech:LearningfromData,byYaserAbuMostafa
●UniversityofToronto:NeuralNetworksforMachineLearning,byGeoffreyHinton
●UniversityofWashington:MachineLearning,byPedroDomingos
●UniversityofWashington:IntroductiontoRecommenderSystems,byJosephKonstan
andMichaelEkstrand
●UniversityofWashington:IntroductiontoDataScience,byBillHowe
Paid Courses
TherearepaidcoursesonMachineLearningofferedbyorganizationssuchasUdemy.Youpay
afeeandhaveaccesstothepremiumcontenttolearnsomethingspecific.
●Udemy:AnIntrotoMachineLearningwithWebData,byHilaryMason
●Udemy:AdvancedMachineLearning,byHilaryMason
●Udemy:IntroductiontoR,byJagannathRajagopal
●Udemy:WorkingwithBigData,byPearson
Other Videos
●MachineLearningCategoryonVideoLectures.Net
●“GettingInShapeForTheSportOfDataScience”TalkbyJeremyHoward
●FacebookTechTalk:PeterNorvigonbigdata
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University Course Material
Thereisapopulartrendfortopleveltechnicaluniversitiestoputcoursematerialsonline
includinglecturevideos,slides,homeworkandassignments.Thismaterialcanbeusedfor
selfstudy.
Someuniversitiesmakethematerialseasiertofindthanothers,MITisashininglightinthis
regardwiththeirOpenCourseWareinitiative.
Undergraduate Level
●MIT6.034ArtificialIntelligence(provideamachinelearningfocus)
●MIT15.075StatisticalThinkingandDataAnalysis
●StanfordCS229MachineLearning(SEEsite)
●StanfordStatistics315aModernAppliedStatistics:ElementsofStatisticalLearning
●StanfordStatistics315bModernAppliedStatistics:ElementsofStatisticalLearningII
●CaltechLearningfromData
Graduate Level
●MIT6.867MachineLearning
●MIT6.825TechniquesinArtificialIntelligence(relatedmachinelearningtopics)
●MIT9.520StatisticalLearningTheoryandApplications
●MIT9.641IntroductiontoNeuralNetworks
●MIT15.097Prediction:MachineLearningandStatistics
●MIT18.465TopicsinStatistics:StatisticalLearningTheory
●HarvardCS281IntelligentMachines:Perception,Learning,andUncertainty(alsoCS181)
●CornellCS6784AdvancedMachineLearning
●CMU10701MachineLearning(videoshereandhere)
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Software and Libraries
Therearealotofsoftwareandlibrariesthatyoucoulduseformachinelearning.
Belowaresomebestofbreedsoftwaretoolsandlibrariesthatareusefulforlearningand
practicingmachinelearning.
●WEKA(GUI,Java)
●R
●ScikitLearn(Python)
●Octave(anopensourceMatLab)
●BigML(inthebrowser)
Ifyouarejuststartingout,IrecommendusingtheWekagraphicaluserinterface.Forexample,
youcanrunyourfirstclassifierin5minutesflat.
Ifyouarestrugglingwithwhichprogramminglanguagetouse,checkoutmypost:
BestProgrammingLanguageforMachineLearning.
IfyouareaJavaprogrammeryoumaybeinterestedinmypost:JavaMachineLearning.
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Competitions
CompetitionsarecommonwithArtificialIntelligenceandMachineLearningconferences.Takea
lookatthewebpagesforsomeofthepopularconferencesandyouwillverylikelyfindcurrent
activemachinelearningcompetitions.
Competitivemachinelearningcanbeagreatwaytolearnnewdatapreparationandmodelling
techniques.Peopleinandaroundcompetitivemachinelearningcanprovideawealthoftips,
resourcesanddifferentwaysofapproachingthesameproblem.Thecompetitionscanalsobea
greatwaytotestoutmethodsandideas.
●Kaggle
●TunedIT
●CrowdAnalytix
●InnoCentive
●Challenge.gov
●KDDCup
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Guides
Ihaveapassionforhelpingprogrammersandengineersgetstartedandkickasswithmachine
learning.Youcanshortcutyourmachinelearningjourneywithanumberoftheguidesand
coursesthatIhavecreatedforyou.
Beginner
●SelfStudyGuidetoMachineLearning:(StartHere!)Discoverthestructuredframework
forselfstudyingmachinelearningthatincludes4competencylevelsandfocused
objectivesandactivitiesforeachlevel.
●MachineLearningFoundations:Discovertheconceptsanddefinitionsofmachine
learningandhavetheconfidencetoexplainittofriendsandcolleagues.
●ConquerSelfLimitingBeliefsinMachineLearning:Discoveryourownselflimitingbeliefs
thatarehaltingyoufromgettingstartedormakingprogressinthefieldofmachine
learning.
●MachineLearningMatters:Discoverwhymachinelearningmatterstoyouandwhyit
matterstotheworld.
Novice
●AppliedMachineLearningProcess:Discoverthestructuredstepbystepprocessfor
applyingmachinelearningtoyourownproblemsnowandinthefuture.Includesaclear
6stepframeworkwithactivitiesandquestionstoanswerateachstepalongtheway.
●JumpStartScikitLearn:DiscoverthePythonmachinelearninglibraryscikitlearninthis
lightweightrecipebook.Contains35recipesreadytocopyandpastefordatahandling,
supervisedlearning,regularizationalgorithms,ensemblemethodsandadvancedtopics.
●JumpStartWeka:DiscovertheWekamachinelearningworkbenchincluding
stepbysteptutorialsforanalyzingdata,applyingmachinelearningalgorithmsand
designingandinterpretingmachinelearningexperiments.
●BeginningWeka:[VideoCourse]Discovertheprocessofappliedmachinelearningwith
stepbysteptutorialsandworkedcasestudyproblemsusingtheWekamachinelearning
workbench.Thefeatureofthiscoursearethe3realworldcasestudieswithstepbystep
tutorialsandvideos.
Intermediate
●SmallProjectsMethodology:Discovertheblueprintforlearningandpracticingapplied
machinelearningwith4projecttypesand90projectideas.
●AlgorithmDescriptionTemplate:Discoverastrategyforlearningamachinelearning
algorithmfast.Iusedthisstrategytolearnanddescribe45natureinspiredalgorithms
thatIturnedintoabook.
●CleverAlgorithms:NatureInspiredProgrammingRecipes:Discover45natureinspired
algorithmsdescribedconsistentlyusingastructuredalgorithmtemplate.Allalgorithms
includeaworkingimplementationinRuby.
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Jet Fuel
●SuperBundle:[GetItAll!]Inthisbundleyougetacopyofallcurrentandallfuture
standalonemachinelearningmasteryguidesandcourses.Asapartofthissuperbundle
youwillbeemailedasnewguidesareaddedinthefuturesothatyoucandownloadthem
atnoextracost.
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Connect With Me!
Hey,mynameisJason.I’mfromAustraliaandIhaveaMastersandPhdinArtificialIntelligence,
I’vewrittenbooksonalgorithms,consultedforstartupsandIworkontropicalcycloneforecasting
systems.Igetalotofsatisfactionhelpingprogrammersmaketheirstartandkicksomeasswith
machinelearning.
Iam33yearsold,marriedwithayoungsonandinmyfreetimeIliketoreadbooks,code,write
articlesandparticipateinmachinelearningcompetitions.
Youcanlearnmoreaboutmeandmystorybyclickinghere.
Reachouttome,I’dlovetohearfromyouandyourgoalswithmachinelearning.
Contactmeviaemailonjason@MachineLearningMastery.com
Followmeon:
LinkedIn: https://www.linkedin.com/in/jasonbrownlee
Twitter: http://twitter.com/TeachTheMachine
Facebook: http://www.facebook.com/pages/MachineLearningMastery/1429846323896563
Google+: http://plus.google.com/+MachineLearningMasteryHome
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