machine learning and analytics for pragmatists

Machine Learning For PragmatistsPresented by Aesa Kamar

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Agenda

1. Data Landscape

2. Conceptual Example

3. Theory in Practice

4. Machine Learning Workflow

5. Demo


Data Science

The Data Landscape

Reporting

Statistical Analysis

Business Intelligence

Machine Learning+

Data Mining


Illustrative Example

We’re sending a team of Scientists to collect Plant

Data from diverse locations


Types of Problems ML Solves

I have a lot of data

I need to understand if there are any trends in my

dataset, cluster them and assign

label

What might good labels for

this data be?

I need to match the data with a model and be able

to predict something from new data

Which label should I assign

to this data?

I don’t know about my output…

I know about my expected output!


Problems ML Is Good AtI don’t know about my

output…I know about my expected

output!

4 4.5 5 5.5 6 6.5 7 7.5 8 8.500.5

11.5

22.5

33.5

44.5

5 Chart Title

4 4.5 5 5.5 6 6.5 7 7.5 8 8.50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Obtained from Iris Dataset

It gets more difficult to visualize with more dimensions

Cluster Predict

https://plot.ly/~AesaKamar/1.embed?share_key=y4Rxlo8UebVG9eiCbrMCsB


ML Is All About FeaturesPetal Width

Petal Height

Color

2 2 Yellow

2.1 2.1 Yellow

1.5 1.8 Yellow

Petal Width

Petal Height

Color

4 4 Pink

4.1 4.1 Pink

3.8 3.8 Pink

(Columns)


Data Flow

Data becomes less messy!

ML needs well-structured dataWe aggregate and cleanse

data so it is more useful to us

We start with a huge amount of diverse data


Machine Learning Workflow

Get Data from a diverse array of sources in large volume

Train Model using a portion of the data and

input parametersExtract Features useful for training and relevant to the problem

Aggregate and Cleanse data, correcting for errors and fixing the schema

Test Model using the remaining dataset and

new inputs


Building an Application

Using F#, C#, and a statistical library called Accord.NET, lets build an AI that can learn to beat you at a simple game of Rock Paper Scissors!


Demo Road Map

Get input data from user in

the form of matchesPredict user’s next input and use data for future training

Aggregate a list of the user’s past games

Extrapolate matches into a table of match histories

Train our learning model with our collected data

Test out learning model against subset of collected

data

Live Demonstration


Data Science and Business Intelligence Takeaways

ExploreUnderstan

d Act

Questionstallan.com/careers

Aesa [email protected]@Tallan.com

machine learning and analytics for pragmatists

Data & Analytics