1. Intro to Machine Learning (And Azure)
5/16/2019 4:16 AM
Intro to Machine Learning (And Azure)
David Crook
@Data4Bots
Questions:
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Agenda The ‘me’ slide Things to know Goals Content Summary
Continuous Q&A

3. David Crook Microsoft Developer Evangelist
Focused on Data and Embedded Systems
Cool Projects I’ve worked on
NASCAR – Racing Feedback App
Home Depot – 135 apps ported in 6 months
JP Morgan & Chase – Trading Floor Rendering Systems
Real Time Oil Intelligence Systems Prototype
Real Time Conversational Engine Prototype
These days: data driven embedded systems
Likes Fishing + Brazilian Jiu Jitsu
Typically not together
(980)
@Data4Bots

4. Stuff you should Know About
Additional Resources
Channel9:
Hackster:
Stack Overflow:
Great Book: Machine Learning Projects for .Net Developers by Mathias Brandewinder
BizSpark
Free Azure
Free Tools Network Partner Give to your startups for free
Microsoft Innovation Center
Hack Tuesdays: Mentoring every Tuesday 11am – 4pm
Free Co-Working for Startups: to apply
Free Training: See schedule here:

5. Goals of Presentation Data Types and Acquisition The Data Lifecycle
Machine Learning
Hands on Lab – Predicting Home Values

6. Data Acquisition 3rd Party Proprietary Mined
Acquired from external sources
Proprietary
Acquired from owned applications
Mined
Acquired through manipulation.
Sourced typically from a combination of 3rd Party and Proprietary.

7. Types of Data Schematic Non Schematic Document Binary
Typed with rules governing storage
Non Schematic
Typed with no rules
Document
Type of non schematic, but each entry is a document instead of a row.
Commonly json format
Binary
Think images etc.

8. The Data Lifecycle  Transform + analyze Visualize + decide Data
5/16/2019 4:16 AM
The Data Lifecycle
VISUALIZE + DECIDE
Applications
Reports
Dashboards
Ask
Mobile
Transform + analyze
Visualize + decide
Data
TRANSFORM + ANALYZE
Orchestration
Information management
Query
Prediction
Collect + manage
Acquire
3rd Party, Proprietary, or Mined
Manage
Maintain through various storage paradigms
Drive intelligence
Derive value from the data
Archive
Storage of very cold data
Destroy
These days, primarily just sensitive data,
COLLECT + MANAGE
Relational
Non-relational
Streaming
Internal & external
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© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9. Intro to Machine Learning and AI
Part of Data Lifecycle: Drive Intelligence
What is Machine Learning
Classification
Regression
Selection
Machine Learning
Have a calculated model based on a data set
Receive a question
Put question parameters through model
Receive a response

10. The Scientific Method Make Observations Think of Interesting Questions
Formulate Hypothesis
Develop Testable Predictions
Gather Data to Test Predictions
Refine, Alter, Expand or Reject Hypothesis
Develop General Theories

11. Hands on Lab

12. Download the Code!

13. Predicting Home Values
Data Mining
Data Storage
Machine Learning
Web Api
Back Story
Moved to Florida
Want to buy a house

14. Solution Architecture

15. 5/16/2019 4:16 AM
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. Hands on Lab – Azure Machine Learning
Read Data in from SQL Azure
Filter out some bad data
Project out Features we don’t want
Train 2 competing models
Evaluate them against each other.
Transform into production System.
Publish as a web end point with api key.
Notice nice documentation.

17. 5/16/2019 4:16 AM
Questions?
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18. dacrook@Microsoft.com @Data4Bots www.indiedevspot.com
5/16/2019 4:16 AM
@Data4Bots
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.