** 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

2. NEED FOR MACHINE LEARNING
WHAT IS MACHINE LEARNING?
MACHINE LEARNING PROCESS
TYPES OF MACHINE LEARNING
DEMO
TYPE OF PROBLEMS SOLVED USING MACHINE LEARNING
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MACHINE LEARNING DEFINITIONS

3. NEED FOR MACHINE LEARNING
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4. “Over2.5quintillionbytesofdataarecreatedeverysingleday,andit'sonlygoingtogrowfromthere.By2020,it'sestimatedthat1.7MB
ofdatawillbecreatedeverysecondforeverypersononearth."
CloudData InternetOfThings SocialMedia
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5. Ranking
Rows Over 75% of what you watch is recommended by Netflix
Recommendations are made by machine learning
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6. Facebook
Tags
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7. Amazon
Alexa
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8. Spam
Filtering
https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
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9. IncreaseinData
Generation
ImproveDecisionMaking
Uncoverpatterns&
trendsindata
Solvecomplex
problems
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10. WHAT IS MACHINE LEARNING?
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11. "AcomputerprogramissaidtolearnfromexperienceEwithrespecttosomeclassoftasksTandperformancemeasure
PifitsperformanceattasksinT,asmeasuredbyP,improveswithexperienceE."
WhatIsMachineLearning?
ArthurSamuelfirstcoinedthetermMachineLearningin
theyear1959.
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12. MachinelearningisasubsetofArtificialIntelligence(AI)whichprovidesmachinestheabilitytolearnautomatically&
improvefromexperiencewithoutbeingexplicitlyprogrammed.
AsimplerdefinitionofMachineLearning
Data TrainingtheMachine BuildingaModel PredictingOutcome
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13. MACHINE LEARNING DEFINITIONS
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14. Algorithm:Asetofrulesandstatisticaltechniquesusedtolearnpatterns
fromdata
Model:AmodelistrainedbyusingaMachineLearningAlgorithm.
PredictorVariable:Itisafeature(s)ofthedatathatcanbeusedtopredict
theoutput.
ResponseVariable:Itisthefeatureortheoutputvariablethatneedstobe
predictedbyusingthepredictorvariable(s).
TrainingData:TheMachineLearningmodelisbuiltusingthetraining
data.
TestingData:TheMachineLearningmodelisevaluatedusingthetesting
data.
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15. MACHINE LEARNING PROCESS
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16. TheMachineLearningprocessinvolvesbuildingaPredictivemodelthatcanbeusedtofindasolutionforaProblem
Statement.
MACHINELEARNINGPROCESS
DefineObjective
DataGathering
PreparingData
DataExplorationBuildingaModel
ModelEvaluation
Predictions
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17. Topredictthepossibilityofrainbystudyingtheweatherconditions.
Step1:DefinetheobjectiveoftheProblem
• Whatarewetryingtopredict?
• Whatarethetargetfeatures?
• Whatistheinputdata?
• What kind of problem are we facing? Binary classification?
Clustering?
WeatherForecastusing
MachineLearning
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18. Datasuchasweatherconditions,humiditylevel,temperature,pressure,etcareeithercollectedmanuallyorscarped
fromtheweb.
Step2: DataGathering
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19. DataCleaninginvolvesgettingridofinconsistencies indatasuchasmissingvalues
orredundantvariables.
Step3:PreparingData
• Transformdataintodesiredformat
• Datacleaning
• Missingvalues
• Corrupteddata
• Removeunnecessarydata
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20. DataExplorationinvolvesunderstandingthepatternsandtrendsinthedata.Atthisstagealltheusefulinsightsare
drawnandcorrelationsbetweenthevariablesareunderstood.
Step4:ExploratoryDataAnalysis
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21. AtthisstageaPredictiveModelisbuiltbyusingMachineLearningAlgorithmssuchasLinearRegression,DecisionTrees,
etc.
Step5:BuildingaMachineLearningModel
• MachineLearningmodelisbuiltbyusingthetrainingdataset
• The model is the Machine Learning algorithm that predicts the
outputbyusingthedatafedtoit
TrainingData
MachineLearning
Model
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22. 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
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23. Thefinaloutcomeispredictedafterperformingparametertuningandimprovingtheaccuracyofthemodel.
Step7:Predictions
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24. TYPES OF MACHINE LEARNING
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25. Supervisedlearningisatechniqueinwhichweteachortrainthemachine
usingdatawhichiswelllabelled.
SupervisedLearning
Tom
Tom
Tom
Jerry
Jerry Jerry
LabelledData
Class‘Jerry’
Class‘Tom’
LabelledOutput
Knownoutput
Trainingphase
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26. Unsupervisedlearningisthetrainingofmachineusinginformationthatisunlabeledandallowingthealgorithmtoact
onthatinformationwithoutguidance.
UnsupervisedLearning
UnlabelledData
Understandpatterns&discoveroutputUnknownoutput
Unlabelled Output
Clustersformedbasedonfeature
similarity
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27. ReinforcementLearningisapartofMachinelearningwhereanagentisputinanenvironmentandhelearnstobehave
inthisenvironmentbyperformingcertainactionsandobserving
therewardswhichitgetsfromthoseactions.
ReinforcementLearning
Whattodo?
Agent
Environment
state
reward action
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28. SupervisedvsUnsupervisedvsReinforcementLearning
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29. TYPES OF PROBLEMS SOLVED USING
MACHINE LEARNING www.edureka.co/data-science

30. 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
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31. Regression
ProblemStatement:TostudytheHouseSalesdatasetandbuildaMachineLearningmodelthatpredictsthehousepricingindex.
LinearRegression
algorithm
Predictthehousepricingindex
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32. Classification
ProblemStatement:Studyabankcreditdatasetandmakeadecisionaboutwhethertoapprovetheloanofanapplicantbasedon
hisprofile
KNNalgorithm
Approveloan Rejectloan
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33. Clustering
ProblemStatement:Toclusterasetofmoviesaseithergoodoraveragebasedontheirsocialmediaout
reach
K-meansAlgorithm
PopularMovies Non-popularMovies
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34. DEMO
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35. ProblemStatement:TostudytheSeattleWeatherForecastDatasetandbuildaMachineLearningmodel
thatcanpredictthepossibility ofrain.
Thedatasetcontainsthefollowingvariables:
• DATE=dateoftheobservation
• PRCP=amountofprecipitation,ininches
• TMAX=maximumtemperatureforthatday,indegrees
Fahrenheit
• TMIN=minimumtemperatureforthatday,indegrees
Fahrenheit
• RAIN=TRUEifrainwasobservedonthatday,FALSEifitwas
not
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36. ProblemStatement:TostudytheSeattleWeatherForecastDatasetandbuildaMachineLearningmodel
thatcanpredictthepossibility ofrain.
LogisticRegression
Rain NoRain
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