The document is an introduction to Microsoft Azure Machine Learning (ML) focused on cloud services and machine learning concepts. It highlights key features of Azure ML Studio, such as a browser-based environment, an immutable library of models, and streamlined experiment flows, while outlining the utility of machine learning in making predictions. The document also includes references for further learning and resources available through Microsoft and other platforms.

1. Hands- On
Azure ML
• Getting started with Cloud
• Hands- On Azure ML Studio
Speaker: Yash Yadav (Microsoft Student Partner)

2. What is Cloud

3. • Microsoft Azure is an ever-expanding set of cloud
services to help your organization meet your
business challenges. It’s the freedom to build,
manage, and deploy applications on a massive,
global network using your favorite tools and
frameworks.
What is Azure?

4. “The goal of machine learning is to build
computer systems that can
adapt and learn from their experience.”
-Tom
Dietterich
What is Machine Learning ?

5. ML Walkthrough
Information
Optimization
Value
Difficulty
What
Happened ?
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
Why did it
Happen?
What Will
Happen?
How can we
Make it Happen?

6. When Should I use Machine
Learning?
 Predication is Small
part of experiences
 No Past data
 Many Rules govern
Experience
 Automated Predication is
Core
 Lots of History
 Magic numbers in current
prediction system

7. Machine Learning Concepts
Data
Model
Parameters
Learning Prediction

8. Classification
Algorithm
Regression
Algorithm
1-When will the customer make
another purchase?
2-How many new followers will I
get next week?
1-Will the customer click on the
top link?
2-Which offer should the customer
receive?
Predictive Analysis

9. Regression

10. Regression

11. Azure ML Studio
 Browser-based environment supporting general
users and data scientists
 Immutable library of models including search,
discover, and reuse
 Wide range of features, machine learning
algorithms, and modeling strategies
 Ability to quickly deploy models as Azure web
services to the ML API service
New experiment flow
Streamlined experiment page
New visualization for data tables
ML Studio New enhancements

12. Microsoft Azure
Machine Learning
 Reduced Complexity
 Access Through Web Browser,
no need to install any thing
 Collaborate work with anyone
 Visual composition, easy to use,
No Coding
 Good storage of Algorithm (Use
in Bing search, Xbox..)
 Have good support for R studio,
Python and Jupyter notebook
Load Data
From
Different
Location
Clean
data
Machine
Learning
Algorithms
R and
Python
Language
Web
services

13. Hands-On Begins!!!
Playing with Data
Training a Predictive Model

14. Q/A

15. • docs.microsoft.com/learn/azure/
• edX Courses on Machine learning with Microsoft
Azure ML Studio
Self Learning Resources
Presentation Attribution and References:
• Microsoft Ignite (New Zealand)
• ML by Akshay and few others…