2. Plan
• Francis why
• Personal ML lib evolution & Qmining
• My bias
• Reminder: Data maturity model
• Reminder: ML place
• Tools:
– Flayers philosopgy
– Mlboost
– Digipy (flayers+mlboost+opencv+swing)
– Mlboost for clustering
– Mlboost for Confusion Matrix
– Outliers visualization (semi supervised)
– Session visualization
– Notebook integration integration, pandasm theanets, sklearn, pytrade)

3. KISS & efficient startup bias

4. Francis Evolution in ML
• 2001-2003: Bengio lab: Plearn -> flayers (compete with torch & plearn)
• Industry (2005-…): Mlboost (numpy, sklearn, scipy, maplotlib) -> boost ML
project: extreme prototyping, preprocessing & feature extraction
• QMining -> Mlboost repackaging, Big data-Mining/ML infrastructure in aws
(95% infra/5% ML)
• Nuance -> Mlboots++ (clustering, advances preprocessing)

5. My python bias

8. Flayers options

9. flayers

10. MLboost
• MLboost: Machine Learning boost library
in Python. MLboost main goal is to
speedup any Machine Learning projects
by simplifying data preprocessing, features
selection and data visualisation. Design by
Machine Learning practitioners to let them
do ML...;)

11. Digipy (mlboost & flayers)
• http://fraka6.blogspot.com/2009/07/digipy-
011-hand-digit-real-time-demo-is.html

12. Mlboost visualisation
• http://fraka6.blogspot.com/2013/04/simplif
ying-clustering-visualization.html

16. Mlboost summary
• Numpy & scipy
• Sklearn (Machine learning)
• Pandas (timeseries & stocks access)
• Matplotlib (visualization)
• Argparse (options)
• Improvement/simplification
– Dimention Reduction
– Semi supervised visualization
– Session preprocessing & stats

20. QMining Techno layter

21. Data Maturity

22. Reminder: ML place
• ML place

23. QUESTIONS
francis@qmining.com
hum...