Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
5. OUR MINDSE T
The world is changing rapidly. We are
fascinated by these upheavals. We are tackling
digital transformation - together with our
partners. We enjoy experimentation and
demand to find the best digital solution for any
given Problem!
6. 2011
Wurde die TOWA Digitalagentur gegründet.
28
Jahre ist unser Durchschnittsalter.
84
Mitarbeiter sind bei TOWA beschäftigt.
7. Philipp Freytag v. Loringhoven
D IRECTO R DATA CO NSULTING
Marketeer | Dev | Designer | Gamer | Chef
10. Intelligence
A mental capability, that involves the abilities:
▪ to reason,
▪ plan
▪ solve problems
▪ think abstractly
▪ comprehend complex ideas
▪ learn quickly
▪ learn from experience!
11. Learning
Learning is the transformative process of
taking in data and turning it into information
and knowledge – when internalized and
mixed with what we have experienced so far
– it changes what we „know“ and builds on
what we do.
It‘s based on input, proces and reflection.
13. DEF INITIO N
It is the Science and engineering of
designing intelligent machines, especially
intelligent computer programs.
14.
15. Miss by a faktor of 1000
2018: 33 Zettabytes 1
Until 2025: 175 Zettabytes 1
We just don’t know
1 https://de.statista.com/statistik/daten/studie/267974/umfrage/prognose-zum-weltweit-generierten-datenvolumen/
17. PE TER NORVIG , GOOGLE DIRECTO R RESE ARCH
We don’t have better algorithms. We just
have more data.
18. What he means is:
Better Data != More Data
The issue is that better data does not
mean more data. As a matter of fact,
sometimes it might mean less!
19. E XPERTS MAKE ERRORS ASWELL
The NASA lost 328 million $, because the
systems within a satellite did not use the
same units of measurement thoughout
Quelle: Wikipedia
20. E XPERTS MAKE ERRORS ASWELL
TARGET lost $5.4 billion in Canada, partly
because its inventory system was loaded
with incorrect data.
Quelle: CanadianBusiness
25. NATE SILVER , STATISTIKE R UND PUBLIZIST
Data is useless without context.
26. Machine Learning is a group of algorithms used to recognize structures in
data.
The concept assumes that it is possible to train a model (algorithm) with
data in such a way that it can make decisions.
So what does ML do?
59. QUESTIO N
Let's discuss, what do you think is the
most important reasons passengers
survived the Titanic sinking?
60. What we‘ll do
1. Signup to Microsoft Machine Learning Studio
2. Download the Data: https://tinyurl.com/y6qg2qh8
3. Load the data into ML Studio
4. Let‘s Explore the Data
61. Data Features
▪ pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower
▪ age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
▪ sibsp: The dataset defines family relations in this way...
Sibling = brother, sister, stepbrother, stepsister
Spouse = husband, wife (mistresses and fiancés were ignored)
▪ parch: The dataset defines family relations in this way...
Parent = mother, father
Child = daughter, son, stepdaughter, stepson
Some children travelled only with a nanny, therefore parch=0 for them.
62. What we‘ll do
1. Clean Data
▪ Drop Name,
▪ Ticket
▪ Cabin
2. Convert Text Data to Numerical Data
▪ Sex
▪ Pclass
▪ Embarked
63. Evaluation Meaning
▪ Mean Absolute Error (MAE): The mean value of the absolute errors. (An error is
the difference between the predicted value and the actual value).
▪ Root Mean Squared Error (RMSE): The square root of the average square of the
forecast errors for the test dataset.
▪ Relative Absolute Error: The mean value of the absolute errors relative to the
absolute difference between actual values and the average of all actual values.
▪ Relative Squared Error: The average of squared errors relative to the squared
difference between actual values and the average of all actual values.
▪ Coefficient of Determination: This value, also known as the R square, is a
statistical measure of how well a model fits the data.
64. In other words
Small Errors = Good!
High Coefficient of Determination = Good!
The closer Coefficient of Determination is to 1 the better!
65. Contact me in case of digital!
TO WA. THE D IGITAL GRO W TH CO MPANY
TOWA
▪ Instagram: www.instagram.com/towa.digital/
▪ LinkedIn: www.linkedin.com/company/2099786/
▪ Facebook: www.facebook.com/towa.digital
▪ Web: www.towa-digital.com
PHILIPP
▪ Instagram: instagram.com/ploringhoven/
▪ LinkedIn: linkedin.com/in/philipploringhoven/
▪ Mail: philipp.loringhoven@towa.at