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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

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Data Science Training Playlist: http://bit.ly/data-science-playlist

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Introduction To Machine Learning | Edureka

  1. 1. 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
  2. 2. NEED FOR MACHINE LEARNING www.edureka.co/data-science
  3. 3. “Over2.5quintillionbytesofdataarecreatedeverysingleday,andit'sonlygoingtogrowfromthere.By2020,it'sestimatedthat1.7MB ofdatawillbecreatedeverysecondforeverypersononearth." CloudData InternetOfThings SocialMedia www.edureka.co/data-science
  4. 4. Ranking Rows Over 75% of what you watch is recommended by Netflix Recommendations are made by machine learning www.edureka.co/data-science
  5. 5. Facebook Tags www.edureka.co/data-science
  6. 6. Amazon Alexa www.edureka.co/data-science
  7. 7. Spam Filtering https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/ www.edureka.co/data-science
  8. 8. IncreaseinData Generation ImproveDecisionMaking Uncoverpatterns& trendsindata Solvecomplex problems www.edureka.co/data-science
  9. 9. WHAT IS MACHINE LEARNING? www.edureka.co/data-science
  10. 10. "AcomputerprogramissaidtolearnfromexperienceEwithrespecttosomeclassoftasksTandperformancemeasure PifitsperformanceattasksinT,asmeasuredbyP,improveswithexperienceE." WhatIsMachineLearning? ArthurSamuelfirstcoinedthetermMachineLearningin theyear1959. www.edureka.co/data-science
  11. 11. MachinelearningisasubsetofArtificialIntelligence(AI)whichprovidesmachinestheabilitytolearnautomatically& improvefromexperiencewithoutbeingexplicitlyprogrammed. AsimplerdefinitionofMachineLearning Data TrainingtheMachine BuildingaModel PredictingOutcome www.edureka.co/data-science
  12. 12. MACHINE LEARNING DEFINITIONS www.edureka.co/data-science
  13. 13. 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
  14. 14. MACHINE LEARNING PROCESS www.edureka.co/data-science
  15. 15. TheMachineLearningprocessinvolvesbuildingaPredictivemodelthatcanbeusedtofindasolutionforaProblem Statement. MACHINELEARNINGPROCESS DefineObjective DataGathering PreparingData DataExplorationBuildingaModel ModelEvaluation Predictions www.edureka.co/data-science
  16. 16. Topredictthepossibilityofrainbystudyingtheweatherconditions. Step1:DefinetheobjectiveoftheProblem • Whatarewetryingtopredict? • Whatarethetargetfeatures? • Whatistheinputdata? • What kind of problem are we facing? Binary classification? Clustering? WeatherForecastusing MachineLearning www.edureka.co/data-science
  17. 17. Datasuchasweatherconditions,humiditylevel,temperature,pressure,etcareeithercollectedmanuallyorscarped fromtheweb. Step2: DataGathering www.edureka.co/data-science
  18. 18. DataCleaninginvolvesgettingridofinconsistencies indatasuchasmissingvalues orredundantvariables. Step3:PreparingData • Transformdataintodesiredformat • Datacleaning • Missingvalues • Corrupteddata • Removeunnecessarydata www.edureka.co/data-science
  19. 19. DataExplorationinvolvesunderstandingthepatternsandtrendsinthedata.Atthisstagealltheusefulinsightsare drawnandcorrelationsbetweenthevariablesareunderstood. Step4:ExploratoryDataAnalysis www.edureka.co/data-science
  20. 20. 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
  21. 21. 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
  22. 22. Thefinaloutcomeispredictedafterperformingparametertuningandimprovingtheaccuracyofthemodel. Step7:Predictions www.edureka.co/data-science
  23. 23. TYPES OF MACHINE LEARNING www.edureka.co/data-science
  24. 24. Supervisedlearningisatechniqueinwhichweteachortrainthemachine usingdatawhichiswelllabelled. SupervisedLearning Tom Tom Tom Jerry Jerry Jerry LabelledData Class‘Jerry’ Class‘Tom’ LabelledOutput Knownoutput Trainingphase www.edureka.co/data-science
  25. 25. Unsupervisedlearningisthetrainingofmachineusinginformationthatisunlabeledandallowingthealgorithmtoact onthatinformationwithoutguidance. UnsupervisedLearning UnlabelledData Understandpatterns&discoveroutputUnknownoutput Unlabelled Output Clustersformedbasedonfeature similarity www.edureka.co/data-science
  26. 26. ReinforcementLearningisapartofMachinelearningwhereanagentisputinanenvironmentandhelearnstobehave inthisenvironmentbyperformingcertainactionsandobserving therewardswhichitgetsfromthoseactions. ReinforcementLearning Whattodo? Agent Environment state reward action www.edureka.co/data-science
  27. 27. SupervisedvsUnsupervisedvsReinforcementLearning www.edureka.co/data-science
  28. 28. TYPES OF PROBLEMS SOLVED USING MACHINE LEARNING www.edureka.co/data-science
  29. 29. 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
  30. 30. Regression ProblemStatement:TostudytheHouseSalesdatasetandbuildaMachineLearningmodelthatpredictsthehousepricingindex. LinearRegression algorithm Predictthehousepricingindex www.edureka.co/data-science
  31. 31. Classification ProblemStatement:Studyabankcreditdatasetandmakeadecisionaboutwhethertoapprovetheloanofanapplicantbasedon hisprofile KNNalgorithm Approveloan Rejectloan www.edureka.co/data-science
  32. 32. Clustering ProblemStatement:Toclusterasetofmoviesaseithergoodoraveragebasedontheirsocialmediaout reach K-meansAlgorithm PopularMovies Non-popularMovies www.edureka.co/data-science
  33. 33. DEMO www.edureka.co/data-science
  34. 34. 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
  35. 35. ProblemStatement:TostudytheSeattleWeatherForecastDatasetandbuildaMachineLearningmodel thatcanpredictthepossibility ofrain. LogisticRegression Rain NoRain www.edureka.co/data-science

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