Machine learning algorithms have limits and are better at interpolation than extrapolation. To improve accuracy, organizations are investing heavily in artificial intelligence and machine learning technologies to process vast amounts of data. Accuracy can be optimized by improving algorithms, data engineering, and obtaining more and higher quality data. Regularly refreshing models as new data is collected ensures the models continue to provide useful business insights. However, machine learning is only as useful as the overall business strategy, so the models and strategy must work together to maximize insights. The quest for accuracy through machine learning is an endless process of continuous improvement.

1. Machine Learning
The Endless Quest for Accuracy
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3. Machine Learning in a Few Words

4. Machine Learning Algorithms
have limits, they’re good at
Interpolation not Extrapolation …
What does that mean?
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5. Learning how to drive in a calm city doesn’t
prepare you for …
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6. … that’s the next level … 

8. Forrester Predicts Investment In Artificial Intelligence Will Grow
300% in 2017
Accenture found that 85% of executives plan to
invest extensively in AI-related technology over
the next three years.
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Yet the hype is BIG!
Why?

9. Photo by Patrick Lindenberg on Unspla
The price of 1GB was 500,000$ in 1980
and 0,03$ in 2015

10. There exists a Huge Technology Ecosystem
Hadoop … Spark …
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11. GPU are 8 to 10 times faster than CPU
for Neural Networks Processing
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12. Photo by Markus Spiske on Unspla
We now can quickly process vast amounts of data
This allows Reinforcement and Supervised
learning to achieve incredible accomplishments

13. Reinforcement Learning:
Learn by simulating your environnement

14. Supervised Learning:
Learn thanks to tagged historical data

15. Data
Engineering
Raw Data
Learning
Data
Test Data
Algorithm
Model Accuracy
How does it work?
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To optimise this process: Improve the Algorithms!

16. Improve the Algorithm ex: MNIST
1998 : Linear Classifier -> 12% error rate
2012 : committee of 35 conv. Net -> 0.23%

17. 14 millions+ tagged Images the challenge is to
recognize the objects
2010 : 25% error rate
2015 : 5% error rate

18. Data
Engineering
Raw Data
Learning
Data
Test Data
Algorithm
Model Accuracy
To optimise this process : Improve the Data Engineering!
Photo by Patryk Grądys on Unspla

19. Corrupted
Data Cleansing
Inconsistent
Inaccurrate
Irrelevent
Dirty

20. Feature Engineering
1506335498
Monday 25 September 2017 10:31:38
This is a date in Unix time
It was a Monday Morning
In September
In 2017
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21. Photo by Annie Spratt on Unsplas

22. Data
Engineering
Raw Data
Learning
Data
Test Data
Algorithm
Model Accuracy
To optimise this process : Get More Data!
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23. “Information is the oil of the 21st century, and
analytics is the combustion engine”
Mr. Sondergaard from Gartner in 2011

24. More Data = More Information = Better Accuracy
Demographic
Behavioural
Geographical
Weather
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Open Data

25. Behavior change quickly how do we adapt?
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Refresh your Models as fast as possible
Collect your Data from every where
Use this Data to make better decisions
Collect the results to build better models
Build a Virtuous Cycle

26. Machine Learning gives Great Insights
but Machine Learning is only as good as your
Business Strategy
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27. Example: If you Sell Products Online
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If you optimise your campain on the probability of click on your ads
If you choose to optimise the probability of selling procuct
You will naturaly build models that will avoid Bots
Your Models will be honey pots for Bots
Bots click but don’t buy

28. Photo by Johannes Plenio on Unspla
Let’s Wrap-up
You have Defeated the Algorithm Complexity
Your Data are Cleaner than Ever and Full of Business Insights
You have Gathered all the Data there is
Your models are Refreshed as Fast as Possible
Your Business Strategy Maximises your Model’s Insights and Vice Versa

29. Now you can … Do it again!
Welcome to the Endless Quest for Accuracy.
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