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Predicting the
future as a service
with Azure ML and R
Barbara Fusinska
@BasiaFusinska
About me
Programmer
Machine Learning
Data Solutions Architect
@BasiaFusinska
Agenda
• Machine Learning
• Supervised vs unsupervised learning
• Building blocks
• Azure ML
• Classification/Regression p...
Machine Learning?
Movies Genres
Title # Kisses # Kicks Genre
Taken 3 47 Action
Love story 24 2 Romance
P.S. I love you 17 3 Romance
Rush hou...
Data-based classification
Id Feature 1 Feature 2 Class
1. 3 47 A
2. 24 2 B
3. 17 3 B
4. 5 51 A
5. 7 42 A
Question:
What is...
Data Visualization
0
10
20
30
40
50
60
0 10 20 30 40 50
Rule 1:
If on the left side of the
line then Class = A
Rule 2:
If ...
Chick sexing
Supervised learning
• Classification, regression
• Label, target value
• Training & Test sets
Unsupervised learning
• Clustering, feature selection
• Finding structure of data
• Statistical values describing the data
Machine Learning workflow
Data
preparation
Data split
Machine Learning
algorithm
Trained model Score
Clean
data
Training
d...
Non-ML components
• Data cleansing & transformation
• Splitting data
• I/O
• Model evaluation
• Comparing algorithms
• Alg...
Data -> Predictive model -> Operational web API in minutes
APIML STUDIO
Azure ML data sources
• Built in datasets
• Save datasets
• Uploaded data
• Import data module:
• Web URL via HTTP
• Hive ...
Demo: Income prediction
Simple Machine Learning process
Using built in datasets
Scoring & Evaluation
Machine Learning building blocks
• Built-in components
• Datasets
• Algorithms
• Scoring & Evaluation
• Binary classificat...
Demo: Automobile price
reduction
Data preparation
Training/Test split
Web Service setup
Building a solution
• Regression
• Dataset upload
• Data preparation
• Training vs Testing
• Web service
Demo: Credit risk assessment
Saved dataset
Algorithm comparison
Using R
Comparing solutions
• Reading data from external
source
• Data normalisation
• Using R scripts
Demo: Twitter sentiment analysis
Uploading template
Feature hashing
Feature selection
Cortana Intelligence Gallery
Using Cortana gallery
• Creating experiment from
template
• Feature selection
• Scoring & evaluation of training
and test ...
Other Azure ML
capabilities
• History
• Debugging
• Parameters
• Cross-validation
• Retraining model
• Feature selection
Thank you
BarbaraFusinska.com
@BasiaFusinska
Predicting the Future as a Service with Azure ML and R
Predicting the Future as a Service with Azure ML and R
Predicting the Future as a Service with Azure ML and R
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Predicting the Future as a Service with Azure ML and R

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Presentation given at ODSC London 2016
https://www.odsc.com/london

Predicting the Future as a Service with Azure ML and R

  1. 1. Predicting the future as a service with Azure ML and R Barbara Fusinska @BasiaFusinska
  2. 2. About me Programmer Machine Learning Data Solutions Architect @BasiaFusinska
  3. 3. Agenda • Machine Learning • Supervised vs unsupervised learning • Building blocks • Azure ML • Classification/Regression processes • Training vs Testing • Building a solution • Using R code • Learning from the community
  4. 4. Machine Learning?
  5. 5. Movies Genres Title # Kisses # Kicks Genre Taken 3 47 Action Love story 24 2 Romance P.S. I love you 17 3 Romance Rush hours 5 51 Action Bad boys 7 42 Action Question: What is the genre of Gone with the wind ?
  6. 6. Data-based classification Id Feature 1 Feature 2 Class 1. 3 47 A 2. 24 2 B 3. 17 3 B 4. 5 51 A 5. 7 42 A Question: What is the class of the entry with the following features: F1: 31, F2: 4 ?
  7. 7. Data Visualization 0 10 20 30 40 50 60 0 10 20 30 40 50 Rule 1: If on the left side of the line then Class = A Rule 2: If on the right side of the line then Class = B A B
  8. 8. Chick sexing
  9. 9. Supervised learning • Classification, regression • Label, target value • Training & Test sets
  10. 10. Unsupervised learning • Clustering, feature selection • Finding structure of data • Statistical values describing the data
  11. 11. Machine Learning workflow Data preparation Data split Machine Learning algorithm Trained model Score Clean data Training data Test data
  12. 12. Non-ML components • Data cleansing & transformation • Splitting data • I/O • Model evaluation • Comparing algorithms • Algorithms settings
  13. 13. Data -> Predictive model -> Operational web API in minutes APIML STUDIO
  14. 14. Azure ML data sources • Built in datasets • Save datasets • Uploaded data • Import data module: • Web URL via HTTP • Hive Query • Azure SQL Database • Azure Table • Azure Blob Storage • Data Feed Provider
  15. 15. Demo: Income prediction Simple Machine Learning process Using built in datasets Scoring & Evaluation
  16. 16. Machine Learning building blocks • Built-in components • Datasets • Algorithms • Scoring & Evaluation • Binary classification • I/O • Visualization
  17. 17. Demo: Automobile price reduction Data preparation Training/Test split Web Service setup
  18. 18. Building a solution • Regression • Dataset upload • Data preparation • Training vs Testing • Web service
  19. 19. Demo: Credit risk assessment Saved dataset Algorithm comparison Using R
  20. 20. Comparing solutions • Reading data from external source • Data normalisation • Using R scripts
  21. 21. Demo: Twitter sentiment analysis Uploading template Feature hashing Feature selection
  22. 22. Cortana Intelligence Gallery
  23. 23. Using Cortana gallery • Creating experiment from template • Feature selection • Scoring & evaluation of training and test data
  24. 24. Other Azure ML capabilities • History • Debugging • Parameters • Cross-validation • Retraining model • Feature selection
  25. 25. Thank you BarbaraFusinska.com @BasiaFusinska
  • EricZhou60

    Apr. 26, 2018
  • HYDN

    Apr. 25, 2018
  • DanielHarborne

    Oct. 16, 2016
  • nishantpithia

    Oct. 10, 2016

Presentation given at ODSC London 2016 https://www.odsc.com/london

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