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Introduction To Machine Learning | Edureka

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** Data Science Certification Training: https://www.edureka.co/data-science ** This Edureka's PPT on "Introduction To Machine Learning" will help you understand the basics of Machine Learning and how it can be used to solve real-world problems. The following topics are covered in this session: Need For Machine Learning What is Machine Learning? Machine Learning Definitions Machine Learning Process Types Of Machine Learning Type Of Problems Solved Using Machine Learning Demo YouTube Video: https://youtu.be/BuezNNeOGCI Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

Tecnología
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NEED FOR MACHINE LEARNING WHAT IS MACHINE LEARNING? MACHINE LEARNING PROCESS TYPES OF MACHINE LEARNING DEMO TYPE OF PROBLEMS SOLVED USING MACHINE LEARNING www.edureka.co/data-science MACHINE LEARNING DEFINITIONS
NEED FOR MACHINE LEARNING www.edureka.co/data-science
“Over2.5quintillionbytesofdataarecreatedeverysingleday,andit'sonlygoingtogrowfromthere.By2020,it'sestimatedthat1.7MB ofdatawillbecreatedeverysecondforeverypersononearth." CloudData InternetOfThings SocialMedia www.edureka.co/data-science
Ranking Rows Over 75% of what you watch is recommended by Netflix Recommendations are made by machine learning www.edureka.co/data-science
Facebook Tags www.edureka.co/data-science
Amazon Alexa www.edureka.co/data-science
Spam Filtering https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/ www.edureka.co/data-science
IncreaseinData Generation ImproveDecisionMaking Uncoverpatterns& trendsindata Solvecomplex problems www.edureka.co/data-science
WHAT IS MACHINE LEARNING? www.edureka.co/data-science
"AcomputerprogramissaidtolearnfromexperienceEwithrespecttosomeclassoftasksTandperformancemeasure PifitsperformanceattasksinT,asmeasuredbyP,improveswithexperienceE." WhatIsMachineLearning? ArthurSamuelfirstcoinedthetermMachineLearningin theyear1959. www.edureka.co/data-science
MachinelearningisasubsetofArtificialIntelligence(AI)whichprovidesmachinestheabilitytolearnautomatically& improvefromexperiencewithoutbeingexplicitlyprogrammed. AsimplerdefinitionofMachineLearning Data TrainingtheMachine BuildingaModel PredictingOutcome www.edureka.co/data-science
MACHINE LEARNING DEFINITIONS www.edureka.co/data-science
Algorithm:Asetofrulesandstatisticaltechniquesusedtolearnpatterns fromdata Model:AmodelistrainedbyusingaMachineLearningAlgorithm. PredictorVariable:Itisafeature(s)ofthedatathatcanbeusedtopredict theoutput. ResponseVariable:Itisthefeatureortheoutputvariablethatneedstobe predictedbyusingthepredictorvariable(s). TrainingData:TheMachineLearningmodelisbuiltusingthetraining data. TestingData:TheMachineLearningmodelisevaluatedusingthetesting data. www.edureka.co/data-science
MACHINE LEARNING PROCESS www.edureka.co/data-science
TheMachineLearningprocessinvolvesbuildingaPredictivemodelthatcanbeusedtofindasolutionforaProblem Statement. MACHINELEARNINGPROCESS DefineObjective DataGathering PreparingData DataExplorationBuildingaModel ModelEvaluation Predictions www.edureka.co/data-science
Topredictthepossibilityofrainbystudyingtheweatherconditions. Step1:DefinetheobjectiveoftheProblem • Whatarewetryingtopredict? • Whatarethetargetfeatures? • Whatistheinputdata? • What kind of problem are we facing? Binary classification? Clustering? WeatherForecastusing MachineLearning www.edureka.co/data-science
Datasuchasweatherconditions,humiditylevel,temperature,pressure,etcareeithercollectedmanuallyorscarped fromtheweb. Step2: DataGathering www.edureka.co/data-science
DataCleaninginvolvesgettingridofinconsistencies indatasuchasmissingvalues orredundantvariables. Step3:PreparingData • Transformdataintodesiredformat • Datacleaning • Missingvalues • Corrupteddata • Removeunnecessarydata www.edureka.co/data-science
DataExplorationinvolvesunderstandingthepatternsandtrendsinthedata.Atthisstagealltheusefulinsightsare drawnandcorrelationsbetweenthevariablesareunderstood. Step4:ExploratoryDataAnalysis www.edureka.co/data-science
AtthisstageaPredictiveModelisbuiltbyusingMachineLearningAlgorithmssuchasLinearRegression,DecisionTrees, etc. Step5:BuildingaMachineLearningModel • MachineLearningmodelisbuiltbyusingthetrainingdataset • The model is the Machine Learning algorithm that predicts the outputbyusingthedatafedtoit TrainingData MachineLearning Model www.edureka.co/data-science
Theefficiencyofthemodelisevaluatedandanyfurtherimprovementinthemodelareimplemented. Step6:ModelEvaluation&Optimization • Machine Learning model is evaluated by using the testing data set • Theaccuracyofthemodeliscalculated • Further improvement in the model are done by using techniques likeParametertuning MachineLearningModel www.edureka.co/data-science
Thefinaloutcomeispredictedafterperformingparametertuningandimprovingtheaccuracyofthemodel. Step7:Predictions www.edureka.co/data-science
TYPES OF MACHINE LEARNING www.edureka.co/data-science
Supervisedlearningisatechniqueinwhichweteachortrainthemachine usingdatawhichiswelllabelled. SupervisedLearning Tom Tom Tom Jerry Jerry Jerry LabelledData Class‘Jerry’ Class‘Tom’ LabelledOutput Knownoutput Trainingphase www.edureka.co/data-science
Unsupervisedlearningisthetrainingofmachineusinginformationthatisunlabeledandallowingthealgorithmtoact onthatinformationwithoutguidance. UnsupervisedLearning UnlabelledData Understandpatterns&discoveroutputUnknownoutput Unlabelled Output Clustersformedbasedonfeature similarity www.edureka.co/data-science
ReinforcementLearningisapartofMachinelearningwhereanagentisputinanenvironmentandhelearnstobehave inthisenvironmentbyperformingcertainactionsandobserving therewardswhichitgetsfromthoseactions. ReinforcementLearning Whattodo? Agent Environment state reward action www.edureka.co/data-science
SupervisedvsUnsupervisedvsReinforcementLearning www.edureka.co/data-science
TYPES OF PROBLEMS SOLVED USING MACHINE LEARNING www.edureka.co/data-science
RegressionvsClassificationvsClustering Regression Classification Clustering • Outputisacontinuousquantity • Outputisacategoricalquantity • Assignsdatapointsintoclusters • SupervisedLearning • SupervisedLearning • UnsupervisedLearning • Mainaimistoforecastorpredict • Mainaimistocomputethecategory ofthedata • Mainaimistogroupsimilar items clusters • Eg:Predictstockmarketprice • Eg:Classifyemailsasspamornon- spam • Eg:Findalltransactionswhichare fraudulentinnature • Algorithm:LinearRegression • Algorithm:LogisticRegression • Algorithm:K-means www.edureka.co/data-science
Regression ProblemStatement:TostudytheHouseSalesdatasetandbuildaMachineLearningmodelthatpredictsthehousepricingindex. LinearRegression algorithm Predictthehousepricingindex www.edureka.co/data-science
Classification ProblemStatement:Studyabankcreditdatasetandmakeadecisionaboutwhethertoapprovetheloanofanapplicantbasedon hisprofile KNNalgorithm Approveloan Rejectloan www.edureka.co/data-science
Clustering ProblemStatement:Toclusterasetofmoviesaseithergoodoraveragebasedontheirsocialmediaout reach K-meansAlgorithm PopularMovies Non-popularMovies www.edureka.co/data-science
DEMO www.edureka.co/data-science
ProblemStatement:TostudytheSeattleWeatherForecastDatasetandbuildaMachineLearningmodel thatcanpredictthepossibility ofrain. Thedatasetcontainsthefollowingvariables: • DATE=dateoftheobservation • PRCP=amountofprecipitation,ininches • TMAX=maximumtemperatureforthatday,indegrees Fahrenheit • TMIN=minimumtemperatureforthatday,indegrees Fahrenheit • RAIN=TRUEifrainwasobservedonthatday,FALSEifitwas not www.edureka.co/data-science
ProblemStatement:TostudytheSeattleWeatherForecastDatasetandbuildaMachineLearningmodel thatcanpredictthepossibility ofrain. LogisticRegression Rain NoRain www.edureka.co/data-science
Introduction To Machine Learning | Edureka

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