Machine learning and azure ml studio gabc

Machine learning and azure ml studio gabc

• 1.
  Machine Learning andAzure ML Studio Yogendra Tamang Global Azure BootCamp 16 April, 2016
• 2.
  Outline • Introduction • CaseScenario • Creating Models • Classification • Creating Models on Azure ML • Demo
• 3.
  Machine Learning ? •AI • Learning Algorithm • Lots of examples • Testing and Evaluation
• 4.
  Machine Learning • MachineLearning - Grew out of work in AI - New capability for computers • Examples: - Database mining • Large datasets from growth of automation/web. • E.g., Web click data, medical records, biology, engineering - Applications can’t program by hand. • E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. - Self-customizing programs • E.g., Amazon, Netflix product recommendations - Understanding human learning (brain, real AI).
• 5.
  Machine Learning • Autonomous Helicopter •Autonomous Driving • Face Detection
• 6.
  Autonomous Cars, FacialDetection, NLP..
• 7.
  Azure ML • CreateModel • Get Data • Pre-processing of data • Define Features • Train the Model • Choose and apply learning algorith • Score and Test the model • Predict new automobile prices
• 8.
  Creating Models 1. Createnew Experiment 2. Type in automobile to see Automobile Price Dataset 1. Play Around with datasets 3. Pre-process Data
• 10.
  Case Scenario
• 11.
  Data Variables
• 12.
  Getting data • TitatanicData/Automobile Price Data • Each row for single automobile
• 13.
  Data Visualization
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  Data Pre-Processing
• 15.
  Data Preprocessing • Convertingto Categorical [PassengerId,Survived,Pclass,Sex,Embarked] • Missing Value Scrubber • Renaming • Project Columns • Exclude[PassengerId,Name,Ticket,Cabin]
• 16.
  Random Forest
• 17.
  Training the model •Train Model Module • Left Port for Model, Right port for data • Run experiment
• 18.
  Predict New AutomobilePrices • Score Model • Left Port from Train model • Right Port from Test Data • Run • Evaluate Model
• 19.
  Publishing
• 20.
  Thank you
• 21.
  References • https://azure.microsoft.com/en-us/documentation/articles/machine- learning-create-experiment/