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Azure Machine Learning
(Data Camp)
Mostafa Elzoghbi
Sr. Technical Evangelist – Microsoft
@MostafaElzoghbi
http://mostafa.r...
Session Objectives & Takeaways
• What is Data Science?
• What is Machine Learning?
• Why do we need Machine Learning?
• Az...
Data Science Involves
• Data science is about using data to make decision that drive actions.
• Data science process invol...
Data Science
• Data Science is far too complex
• Cost of accessing/using efficient ML algorithms is high
• Comprehensive k...
What is Machine Learning ?
• Using known data, develop a model to predict unknown data.
Known Data: Big enough archive, pr...
Microsoft & Machine Learning
Bing maps
launches
What’s the best
way to home?
Kinect launches
What does that
motion
“mean”?...
Why Machine Learning?
Machine learning enables nearly every
value proposition of web search.
Image Analyze
Accent Color: Which border color is the best?
Accent Color: Analyze Image
Accent Color: Windows 10 Store
Accent Color: Windows 10 Store
Text Analytics: User reviews
Positive Negative
Microsoft Azure Machine Learning
Make machine learning accessible to every enterprise,
data scientist, developer, informat...
From data to decisions and actions
Value
Transform data into intelligent action
DATA
Business
apps
Custom
apps
Sensors
and devices
INTELLIGENCE ACTION
People
Autom...
Microsoft Azure Machine Learning
• Web based UI accessible from different browsers
• Share|collaborate to any other ML wor...
Fully
managed
Integrated Flexible Deploy in
minutes
No software to install, no
hardware tomanage, all
you need is an
Azure...
Azure Machine Learning Ecosystem
Get/Prepare
Data
Build/Edit
Experiment
Create/Update
Model
Evaluate
Model
Results
Publish...
Blobs and Tables
Hadoop (HDInsight)
Relational DB (Azure SQL DB)
Data Clients
Model is now a web
service that is callable
...
DEMO
Azure Machine Learning Studio
EXAMPLE
50°F 30°F 68°F 95°F1990
48°F 29°F 70°F 98°F2000
49°F 27°F 67°F 96°F2010
? ? ? ?2020
… … … ……
Known data
Model
Unknown data...
Model (Regression)
90°F
-26°F
50°F 30°F 68°F 95°F1990
48°F 29°F 70°F 98°F2000
49°F 27°F 67°F 96°F2010
Using known data, de...
EXAMPLE
Model (Decision Tree)
Age<30
Income >
$50K
Xbox-One
Customer
Not Xbox-One
Customer
Days Played >
728
Income >
$50K
Xbox-On...
EXAMPLE
Classify a news article as (politics, sports, technology, health, …)
Politics Sports Tech Health
Model (Classification)
Us...
Known data (Training data)
Using known data, develop a model to predict unknown data.
Documents Labels
Tech
Health
Politic...
Known data (Training data)
Using known data, develop a model to predict unknown data.
LabelsDocuments
Feature
Documents La...
Feature vector
Known data
Data instance
i.e.
{40, (180, 82), (11,7), 70, …..} : Healthy
Age Height/Weight
Blood Pressure
H...
Developing a Model
Using known data, develop a model to predict unknown data.
Documents Labels
Tech
Health
Politics
Politi...
Model’s Performance
Known data with true labels
Tech
Health
Politics
Politics
Sports
Tech
Health
Politics
Politics
Sports
...
Steps to Build a Machine Learning Solution
1
Problem
Framing
2
Get/Prepare
Data
3
Develop
Model
4
Deploy
Model
5
Evaluate ...
Example use cases
Machine Learning Algorithms
• ML Algorithm defines how your model will react
• Which Algorithm to use? Depends on:
• Data ...
Machine Learning Algorithms
You can develop solutions by using
• Custom algorithms written in R | Python
• Ready to use ML...
Machine Learning Algorithms
Two major category of algorithms
• Supervised
• Unsupervised
Most commonly used machine learni...
Common Classes of Algorithms
(Supervised|Unsupervised)
Classification Regression Anomaly
Detection
Clustering
Supervised S...
Why you need to know these algorithms?
• If you want to answer a YES|NO question, it is classification
• If you want to pr...
Classification
Scenarios:
 Which customer are more likely to buy, stay, leave (churn analysis)
 Which transactions|actio...
Clustering
Scenarios:
 Customer segmentation: divide a customer base into groups of
individuals that are similar in speci...
Regression
Scenarios:
 Stock prices prediction
 Sales forecasts
 Premiums on insurance based on different factors
 Qua...
Regression versus Classification
Does your customer want to predict|estimate a number (regression)
or apply a label|catego...
Binary versus Multiclass Classification
Does your customer want a yes|no answer?
• Binary examples
• click prediction
• ye...
DEMO
Machine Learning Basics Infographic
References
• Free e-book “Azure Machine Learning”
• https://mva.microsoft.com/ebooks#9780735698178
• Azure Machine Learnin...
HOL Document
• Access Azure HOL Doc: https://aka.ms/azuremlhol
Thank you
• Check out my blog for Azure ML articles: http://mostafa.rocks
• Follow me on Twitter: @MostafaElzoghbi
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Azure Machine Learning

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A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.

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Azure Machine Learning

  1. 1. Azure Machine Learning (Data Camp) Mostafa Elzoghbi Sr. Technical Evangelist – Microsoft @MostafaElzoghbi http://mostafa.rocks
  2. 2. Session Objectives & Takeaways • What is Data Science? • What is Machine Learning? • Why do we need Machine Learning? • Azure ML studio platform capabilities. • Machine Learning basic concepts & algorithms: • How to build a model • Supervised vs Unsupervised models • Evaluate a model. • ML algorithms: Regression, classification, clustering and recommendations.
  3. 3. Data Science Involves • Data science is about using data to make decision that drive actions. • Data science process involves: • Data selection • Preprocessing • Transformation • Data Mining • Delivering value from data : Interpretation and evaluation
  4. 4. Data Science • Data Science is far too complex • Cost of accessing/using efficient ML algorithms is high • Comprehensive knowledge required on different tools/platforms to develop a complete ML project • Difficult to put the developed solution into a scalable production stage • Need a simpler/scalable method: Azure Machine Learning Service
  5. 5. What is Machine Learning ? • Using known data, develop a model to predict unknown data. Known Data: Big enough archive, previous observations, past data Model: Known data + Algorithms (ML algorithms) Unknown Data: Missing, Unseen, not existing, future data
  6. 6. Microsoft & Machine Learning Bing maps launches What’s the best way to home? Kinect launches What does that motion “mean”? Azure Machine Learning GA What will happen next? Hotmail launches Which email is junk? Bing search launches Which searches are most relevant? Skype Translator launches What is that person saying? 201420091997 201520102008
  7. 7. Why Machine Learning?
  8. 8. Machine learning enables nearly every value proposition of web search.
  9. 9. Image Analyze
  10. 10. Accent Color: Which border color is the best?
  11. 11. Accent Color: Analyze Image
  12. 12. Accent Color: Windows 10 Store
  13. 13. Accent Color: Windows 10 Store
  14. 14. Text Analytics: User reviews Positive Negative
  15. 15. Microsoft Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
  16. 16. From data to decisions and actions Value
  17. 17. Transform data into intelligent action DATA Business apps Custom apps Sensors and devices INTELLIGENCE ACTION People Automated Systems
  18. 18. Microsoft Azure Machine Learning • Web based UI accessible from different browsers • Share|collaborate to any other ML workspace • Drag & Drop visual design|development • Wide range of ML Algorithms catalog • Extend with OSS R|Python scripts • Share|Document with IPython|Jupyter • Deploy|Publish|Scale rapidly (APIs)
  19. 19. Fully managed Integrated Flexible Deploy in minutes No software to install, no hardware tomanage, all you need is an Azure subscription. Drag, drop and connect interface. Data sources with just a drop down; run across any data. Built-in collection of best of breed algorithms with no coding required. Drop in custom R or use popular CRAN packages. Operationalize models as web services with a single click. Monetize in Machine Learning Marketplace.
  20. 20. Azure Machine Learning Ecosystem Get/Prepare Data Build/Edit Experiment Create/Update Model Evaluate Model Results Publish Web Service Build ML Model Deploy as Web ServiceProvision Workspace Get Azure Subscription Create Workspace Publish an App Azure Data Marketplace
  21. 21. Blobs and Tables Hadoop (HDInsight) Relational DB (Azure SQL DB) Data Clients Model is now a web service that is callable Monetize the API through our marketplace API Integrated development environment for Machine Learning ML STUDIO
  22. 22. DEMO Azure Machine Learning Studio
  23. 23. EXAMPLE
  24. 24. 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 ? ? ? ?2020 … … … …… Known data Model Unknown data Weather forecast sample Using known data, develop a model to predict unknown data.
  25. 25. Model (Regression) 90°F -26°F 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 Using known data, develop a model to predict unknown data. Predict 2020 Summer
  26. 26. EXAMPLE
  27. 27. Model (Decision Tree) Age<30 Income > $50K Xbox-One Customer Not Xbox-One Customer Days Played > 728 Income > $50K Xbox-One Customer Not Xbox-One Customer Xbox-One Customer
  28. 28. EXAMPLE
  29. 29. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Model (Classification) Using known data, develop a model to predict unknown data.
  30. 30. Known data (Training data) Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Documents consist of unstructured text. Machine learning typically assumes a more structured format of examples Process the raw data
  31. 31. Known data (Training data) Using known data, develop a model to predict unknown data. LabelsDocuments Feature Documents Labels Tech Health Politics Politics Sports Process each data instance to represent it as a feature vector
  32. 32. Feature vector Known data Data instance i.e. {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
  33. 33. Developing a Model Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Training data Train the Model Feature Vectors Base Model Adjust Parameters
  34. 34. Model’s Performance Known data with true labels Tech Health Politics Politics Sports Tech Health Politics Politics Sports Tech Health Politics Politics Sports Model’s Performance Difference between “True Labels” and “Predicted Labels” True labels Tech Health Politics Politics Sports Predicted labels Train the Model Split Detach +/- +/- +/-
  35. 35. Steps to Build a Machine Learning Solution 1 Problem Framing 2 Get/Prepare Data 3 Develop Model 4 Deploy Model 5 Evaluate / Track Performance 3.1 Analysis/ Metric definition 3.2 Feature Engineering 3.3 Model Training 3.4 Parameter Tuning 3.5 Evaluation
  36. 36. Example use cases
  37. 37. Machine Learning Algorithms • ML Algorithm defines how your model will react • Which Algorithm to use? Depends on: • Data Quality • Data Size • What you want to predict • Time constraint • Computation power • Memory limits
  38. 38. Machine Learning Algorithms You can develop solutions by using • Custom algorithms written in R | Python • Ready to use ML services from data market • Existing algorithms
  39. 39. Machine Learning Algorithms Two major category of algorithms • Supervised • Unsupervised Most commonly used machine learning algorithms are supervised (requires labels) • Supervised learning examples • This customer will like coffee • This network traffic indicates a denial of service attack • Unsupervised learning examples • These customers are similar • This network traffic is unusual
  40. 40. Common Classes of Algorithms (Supervised|Unsupervised) Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnSupervised
  41. 41. Why you need to know these algorithms? • If you want to answer a YES|NO question, it is classification • If you want to predict a numerical value, it is regression • If you want to group data into similar observations, it is clustering • If you want to recommend an item, it is recommender system • If you want to find anomalies in a group, it is anomaly detection and many other ML algorithms for specific problem
  42. 42. Classification Scenarios:  Which customer are more likely to buy, stay, leave (churn analysis)  Which transactions|actions are fraudulent  Which quotes are more likely to become orders  Recognition of patterns: speech, speaker, image, movement, etc. Algorithms: Boosted Decision Tree, Decision Forest, Decision Jungle, Logistic Regression, SVM, ANN, etc. Classification
  43. 43. Clustering Scenarios:  Customer segmentation: divide a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests, spending habits, etc.  Market segmentation  Quantization of all sorts, such as, data compression, color reduction, etc.  Pattern recognition Algorithms: K-means Clustering
  44. 44. Regression Scenarios:  Stock prices prediction  Sales forecasts  Premiums on insurance based on different factors  Quality control: number of complaints over time based on product specs, utilization, etc.  Workforce prediction  Workload prediction Algorithms: Bayesian Linear, Linear Regression, Ordinal Regression, ANN, Boosted Decision Tree, Decision Forest Regression
  45. 45. Regression versus Classification Does your customer want to predict|estimate a number (regression) or apply a label|categorize (classification)? • Regression problems • Estimate household power consumption • Estimate customer’s income • Classification problems • Power station will|will not meet demand • Customer will respond to advertising
  46. 46. Binary versus Multiclass Classification Does your customer want a yes|no answer? • Binary examples • click prediction • yes|no • over|under • win|loss • Multiclass examples • kind of tree • kind of network attack • type of heart disease
  47. 47. DEMO Machine Learning Basics Infographic
  48. 48. References • Free e-book “Azure Machine Learning” • https://mva.microsoft.com/ebooks#9780735698178 • Azure Machine Learning documentation • https://azure.microsoft.com/en-us/documentation/services/machine- learning/ • Data Science and Machine Learning Essentials • www.edx.org • Azure ML HOL (GitHub): • https://github.com/Azure-Readiness/hol-azure-machine-learning/
  49. 49. HOL Document • Access Azure HOL Doc: https://aka.ms/azuremlhol
  50. 50. Thank you • Check out my blog for Azure ML articles: http://mostafa.rocks • Follow me on Twitter: @MostafaElzoghbi

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