Machine learning and analytics for pragmatists
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Transcript of Machine learning and analytics for pragmatists
Machine Learning For PragmatistsPresented by Aesa Kamar
© 2016 Tallan | All Rights Reserved 2© 2016 Tallan | All Rights Reserved 2
Agenda
1. Data Landscape
2. Conceptual Example
3. Theory in Practice
4. Machine Learning Workflow
5. Demo
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Data Science
The Data Landscape
Reporting
Statistical Analysis
Business Intelligence
Machine Learning+
Data Mining
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Illustrative Example
We’re sending a team of Scientists to collect Plant
Data from diverse locations
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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!
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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
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Obtained from Iris Dataset
It gets more difficult to visualize with more dimensions
Cluster Predict
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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)
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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
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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
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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!
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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
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Data Science and Business Intelligence Takeaways
ExploreUnderstan
d Act
Questionstallan.com/careers
Aesa [email protected]@Tallan.com