This document discusses machine learning and Microsoft Azure ML Studio. It provides an overview of machine learning, the differences between traditional programming and machine learning, and the main types of machine learning problems. It then introduces Azure ML Studio as a web-based environment for building, testing, and deploying machine learning models using common algorithms and languages like R and Python. The document outlines the basic steps to build a machine learning solution and shows how Azure ML Studio allows publishing predictive models as APIs within minutes to apply to various applications and devices.

1. MACHINE LEARNING & AZURE
ML STUDIO
AMOL GHOLAP – PRINCIPAL CONSULTANT
AGHOLAP@GMAIL.COM
TWITTER - @AMOLGHOLAP

2. AGENDA
• Machine learning around us
• An Introduction to Machine Learning
• Traditional Programming Vs Machine Learning
• Microsoft AzureML Studio
• ML Demo using AzureML Studio

3. MACHINE LEARNING AROUND US

4. MACHINE LEARNING OVERVIEW
• Simplest definition: “The goal of machine learning is to program
computers to use example data or past experience to solve a
given problem.” – Introduction to Machine Learning, 2nd Edition, MIT Press
• ML often involves two primary techniques:
• Supervised Learning: Finding the mapping between inputs and
outputs using correct values to “train” a model
• Unsupervised Learning: Finding patterns in the input data (similar to
Density Estimates in Statistics)

5. TRADITIONAL PROGRAMMING VS MACHINE LEARNING
Traditional Programming
Machine Learning
Output
Data
Program
Ouput
Program / Algorithm
Data
Program Can predict output

6. CLASSES OF LEARNING
• Classification
• Binary classification –yes/no, 1/0, male/female
• Multi-class classification –{A, B, C, D}, {1, 2}, {teacher, student}
• Regression
• Predict a real value –temperature, stock value...
• Ranking
• Order items by some criteria –web search
• Clustering
• Partition items into group –twitter posts

7. STEPS TO BUILD A MACHINE LEARNING SOLUTION

8. AZURE ML
• Web only development studio
• Collaborate & work
• Industry Standard Algorithms available out of the box
• Support language “R” & “Python”
• Build, test, and deploy predictive analytics

9. AZURE MACHINE LEARNING WORKSPACE

10. BUSINESS PROBLEM TO BUSINESS VALUE
• HDInsight
• SQL Server VM
• SQL DB
• Blobs & tables
• Local Files
Publish API in minutes
Devices Applications
Dashboards
Data Microsoft Azure Machine Learning API
Storage
space
Web
Microsoft
Azure portal
Workspace
ML
Studio
Business problem Business valueModeling Deployment
• Desktop files
• Excel spreadsheet
• Other data
files on PC
Cloud
Local

12. DEMO

13. Q & A

14. RESOURCES
• http://blogs.technet.com/b/machinelearning/
• http://azure.microsoft.com/en-us/documentation/services/machine-learning/
• https://studio.azureml.net
• https://www.microsoftvirtualacademy.com/en-us/training-courses/getting-started-with-microsoft-azure-
machine-learning-8425
• http://azure.microsoft.com/en-us/documentation/videos/azure-machine-learning-for-software-engineers/
• https://channel9.msdn.com/events/TechEd/Europe/2014/DBI-B218
• https://archive.ics.uci.edu/ml/datasets.html
• https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/

15. Thank you!!