machine learning - why the hype and how it does its magic

Machine Learning

Why the hype & how it does its magic

DevNexus Atlanta

By: Amir Charania on Feb 23rd 2017

Confidential & Proprietary to Daugherty Business Solutions. 2

Agenda

Machine Learning – Why the Hype?

Machine Learning - Fundamentals

Machine Learning - Framework and Process

Machine Learning in Action! Model Building, testing,

deploying

Machine Learning Use Cases


Section 1

Machine Learning – Why the Hype?


Source: http://www.nytimes.com/2004/03/01/business/microsoft-amid-dwindling-interest-talks-up-computing-as-a-career.html?_r=0


Thought leaders like Gartner identify it as a trend

Gartner defines a strategic technology trend as one with the potential for significant impact on the organization.

Factors that denote significant impact include a high potential for disruption to the business, end users or IT, the need for a major investment, or the risk of being late to adopt

Gartner Top 10 Strategic

Technology Trends 2016


Gartner 2015 – Hype Cycle

Machine Learning

It’s made it to the Gartner Hype Cycle


And the hype increased in 2016!

Machine Learning

Gartner 2016 – Hype Cycle


Major players have launched ML platforms

Less than a year ago!


• Increasing data volumes

• Low storage cost Big Data

• Zero startup (hardware & software) cost

• Reduced startup time

• Pay for what you use

• Deployment is a snap

Cloud

• Microsoft, Amazon & Google now have

platforms for Machine Learning

• Ability to play with End-to-End tools for

free!

Major Players

Entering Market

• Ability to learn from the best in the

world

• Availability of open source data sets and

tools to experiment and learn

MOOC

Multiple factors are creating the perfect storm for ML

Democratization of

Predictive Analytics

• Market is ripe for

mainstream

adoption

• Companies with

access to data,

talent & the right

strategy are

poised to win


But reality is that analytics is easier said than done

Source: MITSloan Management Review: Findings from 2016 Data & Analytics Global Executive Study and Research Project

Managing with

analytics is now a

mainstream idea,

though not a

mainstream

practice

https://www.kaggle.com/wiki/DataScienceUseCases


That trend is only increasing

…few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics

Source: MITSloan Management Review: Findings from 2016 Data & Analytics Global Executive Study and Research Project



Section 2

Machine Learning - Fundamentals


ML falls within the Predictive Analytics step of the Analytics

Escalator

DE

SC

RIP

TIV

E

• Reports • Dashboard • Business Intelligence

DIA

GN

OS

TIC

• Queries • Statistical Analysis • Cubes/OLAP Tools

• Machine Learning • Predictive models • Forecasting

• Optimization • Planning

PR

ED

ICT

IVE

P

RE

SC

RIP

TIV

E


What is Machine Learning

“The goal of machine learning is to build computer systems

that can adapt and learn from their experience”

- Tom Dietterich

Thomas G. Dietterich is Emeritus Professor of computer science at Oregon State University. He is one of the founders of the field of machine learning.


Example 1

Based on the size of the Tumor, predict whether the Tumor is Malignant or Benign

Tumor Size Result

3.4 Malignant

4.2 Benign

1.2 Benign

2.3 Benign

5.2 Malignant

4.7 Malignant

Tumor Size Result

1.9 ?

Classification Problem

Feature Label

Observations


Example 2

Based on the size of the house, predict how much the house will sell for?

House Size Sales Price

2400 sq. ft. $230,000

3200 sq. ft. $410,000

1800 sq. ft. $167,000

2100 sq. ft. $225,000

3000 sq. ft. $350,000

2800 sq. ft. $310,000

House Size Sales Price

2200 sq. ft. ?

Regression Problem


Section 3

Machine Learning – Framework & Process


Process for solving real world problems

Ask the right

question

Frame the

question so that

Machine Learning

can be applied

Apply Machine

Learning

Reframe the

ML answer

to real-world

Extract Data

Develop

Model

Deploy Model

Evaluate

Model

Performance

Define Target

Metric

Extract Derived

Features

Select Features Fit Model

Evaluate

Models


Machine Learning Framework

Input Data Function Output

Machine

Learning

Algorithm

y = f(x)

output prediction

function

Feature -

House size


Machine Learning Process

Training

Tumor Size Result

3.4 Malignant

4.2 Benign

1.2 Benign

2.3 Benign

5.2 Malignant

4.7 Malignant

Training Data Set

Training

Features Training

Training

Label

Learned

Model

Testing

Tumor

Size

Result

1.9 ?

Testing

Features

Learned

Model Prediction


What is NOT Machine Learning

Find the average sales price of the home

Calculating the # of Malignant vs Benign tumors

ML Goal: Build predictive models


Section 4

Demo Time

Machine Learning in Action! Model Building, testing, deploying


Use Cases of Machine Learning

Retail

• Customer Churn

• Predicting Customer LTV

• Cross-selling & Recommendations Algorithms

• Market Basket Analysis

Hospitality

• Inventory Management/Dynamic Pricing

Airline

• Proactive Equipment Maintenance

Financial Services

• Fraud Detection

• Credit Risk Source: https://www.kaggle.com/wiki/DataScienceUseCases


Confidential & Proprietary to Daugherty Business Solutions. 24 Source: Microsoft - Dig Deep with Azure Machine Learning

Here is how you select the Machine Learning Algorithm



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