Machine Learning or Data Science are one of today's hottest buzzwords. The scenarios in which Machine Learning can be applied are diverse and can range from predicting football scores to personalised recommendations in online shops to predictive maintenance in manufacturing.
In this session I will present Azure Machine Learning - a service in Microsoft Azure that anyone can use to build predictive models using the provided machine learning algorithms and deploy it as a web service. Here, I will go through an end-to-end workflow in which I will predict the survival chances on the Titanic.
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Would I have survived the Titanic? Machine Learning in Microsoft Azure
1. Would I have survived the Titanic?
Machine Learning in Microsoft Azure
SQL Saturday 409
Olivia Klose
Technical Evangelist, Microsoft
@oliviaklose | http://oliviaklose.com
14. What is Machine Learning?
“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
15. What is Machine Learning?
Methods and Systems that...
adapt
predict new
data
optimise an
action
extract
information
summarise
data
16. What is Machine Learning not?
Methods and Systems that...
do „Garbage-In-
Knowledge-Out“
predict without
data modelling &
feature
engineering
are always perfect
replace
business rules
17. Machine Learning – Warum?
1. Too complex: When you can’t code it.
(e.g. Natural Language Processing, hand writing recognition, Computer
Vision,…)
2. Too much: When you can’t scale it.
(e.g. Spam & fraud detection, healthcare)
3. Too specialised: When you have to
adapt/personalise.
(e.g. Amazon, Netflix)
4. Autonomous: When you can’t track it.
(e.g. AI gaming, robotics)
21. Hm – what?
𝑓 X = y
Input
Matrix/Table
Output
Vector/Column
22. Hm – what?
ℎ X = y
Input
Matrix/Table
Predicted Output
Vector/Column
Hypothesis
23. Data
13.06.2015 SQLSaturday Rheinland 2015
Forecast Temperature Windy Play tennis?
Sunny Low Yes Play
Sunny High Yes Don't Play
Sunny High No Don't Play
Cloudy Low Yes Play
Cloudy High No Play
Cloudy Low No Play
Rainy Low Yes Don't Play
Rainy Low No Play
Sunny Low No ?
𝑓 x = 𝑦
Features / Input:
(Forecast, Temperature, Windy)
e.g. x = sunny, low, yes
Play /
Don‘t Play
24. Säubern, transformieren, Mathe
Forecast Temperature Windy Play tennis?
Sunny Very Low Yes Play
Sunny High Yes Don't Play
Sunny High Kinda Don't Play
Cloudy ? Yes One place
Fleecy
clouds
High No Play
Cloudy Low No Play
Rainy ? Yes Don't Play
Rainy Low No Play
Sunny Low No ?
25. Säubern, transformieren, Mathe
13.06.2015 SQLSaturday Rheinland 2015
[[ 1.000000],
[ -1.000000],
[ -1.000000],
[ 1.000000],
[ 1.000000],
[ 1.000000],
[ -1.000000],
[ 1.000000]]
Forecast Temperature Windy Play tennis?
Sunny Low Yes Play
Sunny High Yes Don't Play
Sunny High No Don't Play
Cloudy Low Yes Play
Cloudy High No Play
Cloudy Low No Play
Rainy Low Yes Don't Play
Rainy Low No Play
Sunny Low No ?
26. Säubern, transformieren, Mathe
13.06.2015 SQLSaturday Rheinland 2015
[[ 1.000000, 0.000000, 1.000000],
[ 1.000000, 1.000000, 1.000000],
[ 1.000000, 1.000000, -1.000000],
[ 2.000000, 0.000000, 1.000000],
[ 2.000000, 1.000000, -1.000000],
[ 2.000000, 0.000000, -1.000000],
[ 3.000000, 0.000000, 1.000000],
[ 3.000000, 0.000000, -1.000000]]
Forecast Temperature Windy Play tennis?
Sunny Low Yes Play
Sunny High Yes Don't Play
Sunny High No Don't Play
Cloudy Low Yes Play
Cloudy High No Play
Cloudy Low No Play
Rainy Low Yes Don't Play
Rainy Low No Play
Sunny Low No ?
32. True Label
Patient is sick. Patient is healthy.
PredictedLabel
Testpositive
Test correctly states that
the patient is sick.
Test incorrectly states
that the patient is sick
(although he/she is
healthy).
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑡𝑝
𝑡𝑝 + 𝑓𝑝
Testnegative
Test incorrectly states
that the patient is
healthy (although being
sick).
Test correctly states that
the patient is healthy.
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑡𝑝
𝑡𝑝 + 𝑓𝑛
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑡𝑝 + 𝑡𝑛
𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛
Is the model any good? Confusion Matrix
13.06.2015 SQLSaturday Rheinland 2015
34. Azure Machine Learning
Make machine learning accessible to
every enterprise, data scientist, developer,
information worker, consumer, and device
anywhere in the world.
35. Azure Machine Learning
HDInsight
SQL Server VM
SQL DB
Blobs & Tables
Cloud
Desktop files
Excel
spreadsheets
Others…
Lokal
ML
Studio
IDE for ML
Web Service
M
Monetise
Storage Account