Machine Learning and Azure ML Studio

Yogendra Tamang

Global Azure BootCamp16 April, 2016

Outline• Introduction• Case Scenario• Creating Models• Classification• Creating Models on Azure ML• Demo

Machine Learning ?• AI• Learning Algorithm• Lots of examples• Testing and Evaluation

Machine Learning• Machine Learning

- 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).

Machine Learning• Autonomous

Helicopter• Autonomous Driving• Face Detection

Autonomous Cars, Facial Detection, NLP..

Azure ML• Create Model• 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

Creating Models1. Create new Experiment2. Type in automobile to see Automobile Price Dataset

1. Play Around with datasets

3. Pre-process Data

Case Scenario

Data Variables

Getting data• Titatanic Data/Automobile Price Data• Each row for single automobile

Data Visualization

Data Pre-Processing

Data Preprocessing• Converting to Categorical [PassengerId,Survived,Pclass,Sex,Embarked]• Missing Value Scrubber• Renaming• Project Columns• Exclude[PassengerId,Name,Ticket,Cabin]

Random Forest

Training the model• Train Model Module• Left Port for Model, Right port for data• Run experiment

Predict New Automobile Prices• Score Model• Left Port from Train model• Right Port from Test Data• Run

• Evaluate Model

Publishing

Thank you

References• https://azure.microsoft.com/en-us/documentation/articles/machine-

learning-create-experiment/