Introduction to Machine Learning on Azure

2. Photo Credits : Konstantinos Poulakos
Introduction to
Machine Learning on Azure
Athens Mar 17, 2017

3. PresenterInfo
1982 I started working with computers
1988 I started my professional career in computers industry.
1996 I started working with SQL Server 6.0
1998 I earned my first certification at Microsoft as Microsoft
Certified Solution Developer (3rd in Greece)
I started my career as Microsoft Certified Trainer (MCT)
with more than 30.000 hours of training until now!
2010 I became for first time Microsoft MVP on Data Platform
I created the SQL School Greece www.sqlschool.gr
2012 I became MCT Regional Lead by Microsoft Learning
Program.
2013 I was certified as MCSE : Data Platform
& MCSE : Business Intelligence
2016 I was certified as MCSE: Data Management & Analytics
Antonios
Chatzipavlis
SQL Server Expert & Evangelist
MCT, MCSE, MCITP, MCPD, MCSD, MCDBA,
MCSA, MCTS, MCAD, MCP, OCA, ITIL-F

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7. PresentationContent
▪ What is Machine Learning?
▪ Machine Learning Concepts
▪ Azure Machine Learning

8. What is Machine Learning?

9. Finds patterns in data
Uses those patterns to predict the future
What is
Machine
Learning?

10. Machine Learning Workflow
Data Model
Contains
Patterns
Finds
Patterns
Recognizes
Patterns
Application
Supplies new data
to see if it matches
known patterns

11. ▪ Asking the Right Question
▪ Choosing what question to ask is the most important part of making Machine Learning
▪ Ask yourself
▪ Do you have the right data to answer this question?
▪ Do you know how you will measure success?
Start with Machine Learning

12. The Machine Learning Lifecycle
Raw Data
Raw Data
Apply Pre-
processing
Modules
Prepared
Data
Apply
Machine
Learning
Algorithms
Iterate until data is ready
Candidate
Model
Iterate to find the best model
Deploy
Model
Chosen
Model
Applications
Applications
Re-create model regularly

13. Machine Learning Concepts

14. ▪ Training Data
▪ The prepared data used to create a model
▪ Supervised Learning
▪ The value you want to predict is in the training data
▪ The data is labeled
▪ The most common
▪ Unsupervised Learning
▪ The value you want to predict in not in the training data
▪ The data is unlabeled
Terminology

15. Categorizing Machine Learning Problems
Regression Classification Clustering

16. ▪ Training data
▪ Choose features
▪ Input training data (70%)
▪ Choose Learning Algorithm
▪ Generate Candidate model
▪ Testing a Model
▪ Input test data (30%)
▪ Generate target values from test data
▪ Compare target values generated from test data with actual target data
Training and Testing Model

17. Azure Machine Learning

18. Azure Machine Learning Trial
Free
Limited to Azure Machine Learning Features
https://studio.azureml.net/home
Add Machine Learning to Azure account
Full Azure Integration
https://portal.azure.com
Azure Machine Learning Account Options

19. Azure Machine Learning
Demo

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