1.
Machine Learning 101
Advanced Analytics and DataScience

2.
CCG
Analytics Solutions & Services
DATA
MANAGEMENT
Data & analytics consultants with a passion for helping clients
overcome business challenges & increase performance by
leveraging modern analytic solutions.
BUSINESS
ANALYTICS
DATA
STRATEGY

3.
VOICES OF OUR CUSTOMERS
“CCGto brought the expertise and the vision of to help us execute, to provide
visibility to the data in a manner that we can use it faster.”
- Gary Gray, Business Solutions Executive, Corsicana Mattress Company
“The people we talked to know us. CCGwasn’t trying to fit us into a boilerplate
template but prescribe a tailored solution. Their RapidRoadmap was the basis of
our BI Strategy for the next two years.”
- Kevin Davis, Sr. Director of BI, Kforce
“Many times with CCG, we come to the table with questions or ideas and within a
couple of days or weeks the team comes back with above and beyond what we
actually asked for. They care.”
- Chris Fitzpatrick, Vice President of Business Analytics & Strategy, vineyard vines
“"I'mamazed at the talent at CCG, not just the skillset - they're really good people.
We've already referred them once and will do so again!”
- CIO, Ruth’s Chris Hospitality Group

4.
Objectives
By the end of this workshop, you should be able to:
Describe what Machine Learning is and how it fits in to the analytic landscape
Understand the difference between traditional and “advanced” analytics
Describe what a statistical model is
Understand a machine learning approach to statistical modeling
The conceptual methodology behind the Machine Learning areas of classification, and clustering
Describe some the most common tools for implementing data science and Machine Learning

5.
AGENDA
Why should anyone care about
machine learning?
What is Machine Learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?

6.
The concepts in Machine Learning are not new.
How has Machine Learning Evolved?
https://www.quantinsti.com/blog/machine-learning-basics
nother human.

7.
Even though the concepts are decades old,
machine learning has only become feasible at scale in recent years.
Why Machine Learning Now?
Flood of data and decreasing costs of storage
Increasing computational power
Increased attention from researchers
Growth of open source technologies
Support from industries

8.
Machine Learning has tons of useful applications you already encounter or
hear about every day.
Analyzing
Images
Understanding
Language
Forming &
Executing Strategy
Personalized
Recommendations
Autonomous
Decisions
Predicting
Asset Values
How is Machine Learning used?

9.
Machine Learning isn’t just applicable to high tech.
There are suitable use cases presentin most business sectors.
Where is Machine Learning used?
Healthcare
• Claims Fraud
• Real-time mortality risk
for ICU patients
• Response Adapted
Radiotherapy
• Predictingpatient
medication adherence
• Translational/precision
medicine
Finance
• Foreclosure/creditrisk
• Risk analysis
• Fraud detection
• Demand forecasting
• Anti Money Laundering
• Algorithmic trading
Energy
• Resource allocation
• Load forecasting
• Grid optimization
• Robotics
• Anomaly detection
• Image recognition
• Predictivemaintenance
Retail
• Singleview of customer
• Customer serviceanalysis
• Inventory planning
• Social media analysis
• Lead scoring
• Marketing campaign
evaluation

10.
Machine learning sits at the intersectionof statistics and computer science to
help businesses make decisions.
Why Machine Learning Now?
Computational
Power
Statistics
Predictive & Prescriptive Decision Support
Faster More
Accurate
More
PowerfulSelf-Improving Always-On

11.
AGENDA
Why should anyone care about
machine learning?
What is Machine Learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?

12.
Machine Learning is a technique that can be used in the data
science process to achieve several possible outputs.
What is Machine Learning?
Data Science
A broadprocessfor generatinginsights
that mayinvolve dataingestionfrom
one or manysources(includingexternal
data, streamingdata, or bigdata), data
processingandcleansing, model
generationusingstatistical ormachine
learningapproaches, model selection,
model deploymentandmaintenance,
and visualizationof data.
Advanced Analytics
Applydatascience topredictive (what
will happen?)orprescriptive(what
shouldwe do?) businessuse cases.
Artificial Intelligence /
Cognitive Computing
Applydatascience toapproximate
humanintuitionanddecisionmaking
(e.g.strategy,creativity,planning) or
humansensoryfunctions(e.g.
computervision,natural language
understanding,etc.)
Statistics
A branchof mathfor generating
descriptionsof orinferencesabouta
population,oftenbasedonsamples
of the population.Inferencesmay
take the form of “models,”which
are equationsthatapproximate the
data’sinherentrelationships.
Machine Learning
Combinescomputerscience with
mathconceptsto generate models
by rapidlyiteratingonlarge
datasets.
Other Analytics Disciplines
(Data Engineering,Visualization)
Disciplines Process Outputs
Automation /
Robotics /
Intelligent
Devices
Actions
Strategy /
Operations

13.
Advanced Analytics (“AA”) enable predictive and prescriptive uses of data by
applying sophisticatedmath and statistics to automate parts of the analysis.
What is Advanced Analytics?
Traditional analytics focuses on
understanding and explaining the
data that has been collected.
AA focuses on generating new
data in the form of predictions or
decisions, and going the extra step
to automate decision-making
when possible.

14.
Advanced Analytics deal with making “best guesses” faster,better,and
more consistentthan relying on human SMEs.
Provide insights on existing data using:
• Raw data points
• Summaries of data
• Calculations across existing data fields
• KPIs
The data reported are historical or current facts.
Generally requires the application of basic
mathematics or arithmetic.
Generate new data, including:
• Predicted future values
• Best guesses of missing values
• Suggested next steps
• Categorizations
The data generated are “best guesses” and
contain some uncertainty.
Requires the application of advanced
mathematics, statistics and computing principles.
TraditionalAnalytics AdvancedAnalytics
Traditional vs. Advanced Analytics

15.
AGENDA
Why should anyone care about
machine learning?
What is Machine Learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?

16.
A model is a repeatable, data-driven approach to making a best guess.
It does this by formalizing mathematical relationships between data in the form of either:
– Rules (e.g. predict applicants will default on a loan if Credit Score < 700 and Debt to Income Ratio > 30%)
– Or an equation (e.g. predict Home Price = 100*Square Footage + 2*Average Income in the Area)
NOTE that this is not the same as a DATA model. These are different things:
Machine Learning works by using “algorithms” to generate “models.”
How does Machine Learning work?
Data Model Statistical Model

17.
In the past we’ve toldcomputers how to use data to a answer our
questions.
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month sales =
(prior month +
2 months prior +
3 months prior)
/ 3
Answer
This month’s sales = $3MM?
What’s a model?

18.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
But we’ve found that if we give the machine historicfacts, we can let it find
the right program/ model to plug in for future answers.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
What’s a model?

19.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Once we have our machine-defined program, we can use it with new data to
make better predictions.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
New Data
Prior month sales: $8MM
2 months prior: $6MM
3 months prior: $8MM
Answer
This month’s sales = $5MM
What’s a model?

20.
A defined set of steps for solving a problem
Often involves repeating steps
May or may not have an ending condition
– The problem is solved to our satisfaction
• For example – stop when the last 4 iterations have been 95% accurate or better
– The problem hasn’t been solved but we don’t seem to be getting any closer to solving it
• For example – stop if the last 10 iterations have not seen any improvement in accuracy
– The process has run for a long time
• For example – stop after the program has run for 12 hours, regardless of whether progress is still being made
The wordalgorithm gets used a lot, but it isn’t always defined.
What is an algorithm?

21.
Collect the data and randomly create initial decision rules.
Design a method for measurably evaluating how good or bad your hypothesis is.
Update your hypothesis in a way that marginally improves the performance of your decision rules.
Continue this process until the hypothesis either you are satisfied with the results, or your hypothesis
can’t improve anymore with the data available.
Almostall machine learning algorithms followthe same general pattern.
Create a
hypothesis
Evaluate the
hypothesis
Adjustthe
hypothesis
Repeat until
convergence
What is an algorithm?

22.
AGENDA
Why should anyone care about
machine learning?
What is Machine Learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?

23.
There are two main families of algorithms to choose from.
Supervised Learning Unsupervised Learning
There aren’t necessarily “right answers,” we just want to
get a better understanding of our data.
We know the “right answers” for some of the scenarios.
– We may have history we can look back on
– We may be hoping to replicate human decision making

24.
Supervisedor Unsupervised?
Predict our profits next quarter. Supervised
Identify the number written on a check.
Group our customers into segments.
Supervised
Unsupervised
Predict a user’s rating for a given product. Supervised
Find the most importantvariables in a dataset. Unsupervised
Identify credit card transactionsthat are out of the ordinary. Unsupervised

25.
Now let’s walkthrough two of the mostpopular machine learning
approaches and discuss how the algorithms are applied.
How does an algorithm really workfor businesses?
Classification Clustering

26.
Use classificationwhen you want to guess a non-numeric value,like a
yes/no answer.We will take a decisiontree approach.
Everyone will repay their loan.
Create a
hypothesis
20 outstanding loans

27.
Use classificationwhen you want to guess a non-numeric value,like a
yes/no answer.We will take a decisiontree approach.
Calculate accuracy as the % of predictions that are correct based on your current set of rules.
Evaluate the
hypothesis
20 outstanding loans
12 repaid, 8 defaulted
Accuracy = 12/20 = 60%

28.
Use classificationwhen you want to guess a non-numeric value,like a
yes/no answer.We will take a decisiontree approach.
Find the next branch by looking for the data split that would have the biggest impact on the purity of
each node. There are several ways to do this mathematically (Gini Index, Information Gain, Chi-
Square).
Adjustthe
hypothesis
20 outstanding loans20 outstanding loans 20 outstanding loans
CreditScore > 700CreditScore < 700 Income > 60kIncome < 60k DTI > 40%DTI < 40%
80%73%70%50%71%53%
59% weighted 60% weighted 75% weighted

29.
Use classificationwhen you want to guess a non-numeric value,like a
yes/no answer.We will take a decisiontree approach.
Repeat the process for each of your new “leaf” nodes. Stop when you reach an acceptable level of
accuracy, or when your accuracy begins getting worse with independent data.
Repeat until
convergence
20 outstanding loans
DTI > 40%DTI < 40%
CreditScore > 700CreditScore < 700Income > $60kIncome < $60k
100%50% 100%100%
80% weighted

30.
Classificationis used for lots of problems that copy human intuition. Think
about how you classify informationto identify these images!
These use cases areobviously
morecomplex than our
simple decision tree, but with
moreadvanced approaches
like convolutionalneural
networks thesepictures can
definitely be classified by a
machine.

31.
Use clustering when there’s no “correct”classification,but you still want to
assign individuals to groups. This algorithmis called k-means clustering.
Imagine Marketing has
asked you to split these
customers into 3 groups.
How would you do it?

32.
Use clustering when there’s no “correct”classification,but you still want to
assign individuals to groups. This algorithmis called k-means clustering.
I can segment my customers by assigning them to 3 groups. We’ll set down 3 random “anchors” and
assign each customer to its closest anchor.
Create a
hypothesis

33.
Use clustering when there’s no “correct”classification,but you still want to
assign individuals to groups. This algorithmis called k-means clustering.
Find the distance between each customer and the center of each group. Take note of which
customers are actually closest to a different center than the one they’re assigned to.
Evaluate the
hypothesis

34.
Use clustering when there’s no “correct”classification,but you still want to
assign individuals to groups. This algorithmis called k-means clustering.
Reassign each customer to the group corresponding to the center they’re closest to, and move the
anchors to the middle of their new group.
Adjustthe
hypothesis

35.
Use clustering when there’s no “correct”classification,but you still want to
assign individuals to groups. This algorithmis called k-means clustering.
Repeat until
convergence
Keep moving the anchors and re-assigning customers until the anchors stop moving.

36.
This is just the tip of the iceberg.There are several
algorithms available for various types of problems.

37.
AGENDA
Why should anyone care about
machine learning?
What is Machine Learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?

38.
Delivering analytics with Machine Learning requires alignment
across people, process,technology,and data.
Engaging with Machine Learning
Image inspired by Microsoft
People
Process Technology
Data
Guide
Support
Enable

39.
Data scientists combine broad skills to integrate data, build
models,and drive business value.
People
Process Technology
Data

40.
Let’s lookat the MicrosoftTeamData Science Process to see how
data scientists spend their time.
People
Process Technology
Data

41.
TraditionalAnalytics
The outputs of the process can be used in traditional analytics,
analyzed directly,or fed into automated decision-making.
Storeand access data. Filter and aggregate it. Visualizeit.
Show it to the business
so they can take action.
MachineLearning
Filter and aggregate it. Create a model. Generate new data
(predictions, etc.).
The new data can be
stored with the rest of the
data for usein analytics.
Or it can be visualized
directly to gain insights.
Or it can automate
decisions or actions,
allowing better processes
to run faster and 24/7.
People
Process Technology
Data

42.
The sources of data for use in data science can be broad.
People
Process Technology
Data
Data
Warehouses
•Curated &
Governeddata
•Big data
•Cloud or on-prem
Data Lakes
•Unstructured&
Semi-structured
data
•Streaming data
•Partiallycurated
Externally
Procured
Data
•Maybe purchased
from 3rd
party
providers
•Maybe scraped
from the web
•Mayrequire
designingresearch
experiments
Data scientists typically havethe
programming and data integration skills to
use data fromanywhereitcan be found.

43.
The Microsofttechnology stackprovides a holistic
solutionto your Machine Learning needs.
People
Process Technology
Data

44.
We can work with your business to deliver custompredictive and
prescriptive analytics across the lifecycle.
What can CCG do?
Use Case Definition
• Develop a backlog of
predictive and
prescriptive use cases
• Refine and prioritize use
cases by value
• Develop a predictive
roadmap
Model Development
• Aggregate data from
across internal and
external data sources
• Develop and test
multiple models to find
the best approach to
making predictions
Model Maintenance
• Monitor and maintain
statistical models to
sustain predictive power
• Develop a model telemetry
dashboard
• Test model design changes
to improve predictive
power
Model Governance & Processes
• Assess existing Data Science capabilities
• Develop standards and processes to help guide data science output
• Build a Data Science Center of Excellence
Model Deployment
• Customize and deploy
pre-existing models from
Azure Cognitive Services
• Deploy custom model as
an API or batch job, or
support deployment in
existing systems
Rapid Insight Prototype Offering Model as a Service Subscription Offering

45.
CCG’s Rapid Insight Solution
Actionable Backlog
– Of use cases ripe for predictive
analytics to transform your
business
Detailed Readouts
– The materials we leave behind
will include extensive analysis
of our methodology, findings,
and recommendations
Ownership of the Model
– Just because the project ends
doesn’t mean the model stops
working. Unlike other managed
service providers, what we
produce on your behalf is yours
to keep
Identify Use Cases
– By holding a workshop with
process SMEs to identify
opportunities to supercharge the
business
Summarize the Findings
– So you can understand the
model’s outputs and begin
taking action on what we’ve
learned
Develop a Prototype Model
– To generate forecasts,
classifications, orexploratory
analysis forone of your use
cases using an industry-standard
tool like Azure Machine Learning
Studio or Databricks
Week 1 Weeks 2-5 Week 6

46.
Fully Operational Production Model
– Available at all times, in production
– Batch & API integrations
Model Supervision
– Model is monitored for ongoing usability
– Performance dashboard
– Guaranteed accuracy SLAs
Model Retraining & Support
– Scheduled & triggered model re-tuning or re-training
– Add new data features over time
Model as a Service Solution
Set up model as
a web service
Visualize model
performance in a
dashboard
Maintain and
enhance model

47.
THANK YOU!
What questions do you have?

48.
Microsoftofferspre-builtAPIs through Cognitive Services that
can expedite the deploymentof AI capabilities.
People
Process Technology
Data

49.
VISUAL DRAG -AND-DROP
Azure Machine Learning Studio

50.
What is Azure Databricks?
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
Best of Databricks Best of Microsoft
Designed in collaboration with the founders of Apache Spark
One-click set up; streamlined workflows
Interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
Native integration with Azure services (Power BI, SQL DW, Cosmos DB, Blob Storage)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise-grade SLAs)

51.
Azure Databricks key audiences & benefits
Unified analytics platform
Integrated workspace
Easy data exploration
Collaborative experience
Interactive dashboards
Faster insights
• Best of Spark & serverless
• Databricks managed Spark
Improved ETL performance
• Zero management clusters, serverless
Easy to schedule jobs
Automated workflows
Enhanced monitoring & troubleshooting
• Automated alerts & easy access to logs
Zero Management Spark
Cluster democratization (serverless)
Fast, collaborative analytics platform
accelerating time to market
No dev-ops required
Enterprise grade security
• Encryption
• End-to-end auditing
• Role-based control
• Compliance
Data scientist Data engineer CDO, VP of analytics
Provided by Microsoft and Databricks under NDA