Slides of the 14 april Montreal-Python user group presentation on "Machine Learning Empowered by Python"

1. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Machine Learning empowered by Python
Francis Pieraut
fpieraut@gmail.com
http://fraka6.blogspot.com/
14 april 2009
Python-Montreal
http://montrealpython.org/
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

2. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Hand writing digit recognition
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

3. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Why a talk about machine learning and python?
Machine Learning 101
Supervised Learning (classification)
Important Concepts
Let’s dive into an example
Why Python?
Data example
Training and Testing (python-mlboost)
Conclusion
Questions?
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

4. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Why Machine Learning empowered by Python?
Integrated real live prototypes ⇒ Probability(success) ⇑
Python allows extreme prototyping
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

5. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Speaker: Francis Pieraut, eng. M.Sc.A.
Master@LISA - Statistical Machine Learning - udm
(flayers: C++ Neural Networks lib)
Industry - 6 years in Machine Learning/AI startups
(mlboost: Python Machine Learning Boost lib)
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

6. Outline
Why a talk about machine learning and python?
Machine Learning 101 Supervised Learning (classification)
Let’s dive into an example Important Concepts
Conclusion
Questions?
AI and Machine Learning
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

7. Outline
Why a talk about machine learning and python?
Machine Learning 101 Supervised Learning (classification)
Let’s dive into an example Important Concepts
Conclusion
Questions?
Machine Learning
Learning from data
Classification vs Clustering
Applications: Fraud detection, Vision, Target-Marketing,
Search Engine, Insurance (Risk) etc.
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

8. Outline
Why a talk about machine learning and python?
Machine Learning 101 Supervised Learning (classification)
Let’s dive into an example Important Concepts
Conclusion
Questions?
Supervised Learning (need class tag for each example)
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

9. Outline
Why a talk about machine learning and python?
Machine Learning 101 Supervised Learning (classification)
Let’s dive into an example Important Concepts
Conclusion
Questions?
Important Concepts
Datasets (features + class)
Generalization vs Overfitting
Classification vs Clustering
Features Quality (invariant and informative)
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

10. Outline
Why a talk about machine learning and python?
Why Python?
Machine Learning 101
Data example
Let’s dive into an example
Training and Testing (python-mlboost)
Conclusion
Questions?
Why Python? Prototyping Extreme Efficiency
C++, sed, awk...⇒ N years
Python ⇒ N weeks
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

11. Outline
Why a talk about machine learning and python?
Why Python?
Machine Learning 101
Data example
Let’s dive into an example
Training and Testing (python-mlboost)
Conclusion
Questions?
Hand writing digit recognition - Data examples
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

12. Outline
Why a talk about machine learning and python?
Why Python?
Machine Learning 101
Data example
Let’s dive into an example
Training and Testing (python-mlboost)
Conclusion
Questions?
Hand writing digit recognition - Training
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

13. Outline
Why a talk about machine learning and python?
Why Python?
Machine Learning 101
Data example
Let’s dive into an example
Training and Testing (python-mlboost)
Conclusion
Questions?
Digit recognition (Demo)
Naive Machine Learning: Training on raw pixels
Testing example visualization
Real-time demo
Naive machine learning limitations
Feature extraction visualisation
Training on new features
Real-time demo
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

14. Outline
Why a talk about machine learning and python?
Why Python?
Machine Learning 101
Data example
Let’s dive into an example
Training and Testing (python-mlboost)
Conclusion
Questions?
Want to try the Demo or contribute?
try it easy install digipy (soon)
http://bitbucket.org/fraka6/digipy/
http://bitbucket.org/fraka6/mlboost
http://bitbucket.org/fraka6/flayers
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

15. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
What you should remember?
Machine learning algorithms applications ⇑
goal = generalization⇒good prediction (DON’T OVERFIT)
80-90% pre or post-processing + data visualization
Python provide amazing integration
try it, easy install digipy
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python

16. Outline
Why a talk about machine learning and python?
Machine Learning 101
Let’s dive into an example
Conclusion
Questions?
Any questions?
...
A special thanks to Yannick Gingras help for the UI and the
packaging, Pierre-Alexandre Fournier and Jeremy Barnes feedbacks
and Caroline patience
easy install digipy
http://fraka6.blogspot.com/
icml2009 - Internation Conference on Machine Learning
(June 14-18, Montreal 2009)
..
Thanks,
Francis Pieraut - alias Fraka6
fpieraut@gmail.com
Francis Pieraut fpieraut@gmail.com http://fraka6.blogspot.com/ Machine Learning empowered by Python