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Predictive Analytics and Machine Learning 101 Poya Manouchehri @PoyaManouchehri NEUROMINE

Predictive Analytics and Machine Learning 101

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Page 1: Predictive Analytics and Machine Learning 101

Predictive Analytics and Machine Learning 101Poya Manouchehri@PoyaManouchehri

NEUROMINE

Page 2: Predictive Analytics and Machine Learning 101

WHAT IS PREDICTION?

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WHAT IS LEARNING?

• CONNECTING THE DOTS (INTERPOLATION AND EXTRAPOLATION)

• NOT EXACT• PROCESS• EXPERIMENTATION / TRIAL AND ERROR

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WHAT IS INTELLIGENCE?

• LEARNING• REASONING• INSTINCT• HIGHER NATURE

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MACHINE LOGIC VS LEARNING

IF TEMPERATURE IS LOWAND IT IS OVERCASTAND IT RAINED YESTERDAYTHEN IT WILL RAIN TODAY

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TYPES OF PROBLEMS

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REGRESSION AND CLASSIFICATION

ModelIndependent Variable

Dependent Variable

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CLUSTERING

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MACHINE LEARNING PROCESS

Data(Independent and

Dependant Variables)

Training Model

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DECISION TREES

INPUT:• EYES EXIST• NUMBER OF LEGS• FURRINESSOUTPUT:• CAT, PERSON, CHAIR, OTHER

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DECISION TREES

SAMPLE DATA:• YES, 4, FURRY CAT• YES, 2, NOT FURRY PERSON• NO, 2, NOT FURRY OTHER• …

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4<4 >4

Number of Legs

Other

Furry Not Furry

Cat

Furriness

Chair

Yes

Eyes Exist

No

OtherPerso

n

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NEURAL NETWORKS

Neuron

Synapse

Neuron

Neuron

Synapse

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NEURAL NETWORKS

Actual

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NEURAL NETWORKS – FEATURE DETECTORS

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NEURAL NETWORKS - XOR

Input 1 Input 2 Output0 0 00 1 11 0 11 1 0

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NEURAL NETWORKS - XOR

0.000.100.200.300.400.500.600.700.800.901.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.00

0.20

0.40

0.60

0.80

1.00

XOR

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

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CHALLENGES – GOOD DATA

• NOT ENOUGH DATA• UNREPRESENTATIVE DATA• NO REAL RELATIONSHIPS IN DATA

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CHALLENGES – GENERALISATION

Output

Input

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CHALLENGES – LOCAL MINIMA

Error

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APPLICATIONS – IMAGE / VOICE RECOGNITION

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APPLICATIONS – HEALTH

• DIAGNOSIS (E.G. CANCER)• PATIENT RE-ADMISSION

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APPLICATIONS – PERSONALIZATION

• TARGETED MARKETING• RECOMMENDATION• CHURN ANALYSIS• LEAD SCORING

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APPLICATIONS – FINANCE

• PAYMENT FRAUD DETECTION• STOCK PRICE PREDICTION• INVENTORY MANAGEMENT• SALES PREDICTION

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APPLICATIONS – ANOMALY DETECTION

• DETECTING FAULTY HARDWARE (IN SERVER FARM)• INTRUDER ALERT• SPAM FILTERS

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SOFTWARE AND SERVICESOPEN-SOURCE• R PROGRAMMING LANGUAGE• WEKA• ORANGE

COMMERCIAL• MATHEMATICA• MATLAB• SQL SERVER ANALYSIS SERVICES (SSAS)

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SOFTWARE AND SERVICESSERVICES• GOOGLE PREDICTION API• AZURE ML• NEUROMINE• BIGML• WISE.IO• ALGORITHMS.IO• INFER.COM...

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RESOURCESCOURSERA:• GEOFFREY HINTON - NEURAL NETWORKS FOR MACHINE

LEARNING

DATA SOURCES:• UCI MACHINE LEARNING REPOSITORY• AUSTRALIAN BUREAU OF STATISTICS

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QUESTIONS