PyData Berlin 2014 Keynote: Commodity machine learnin

Transcript

1. importamueller@gmail.com @t3kcit @amueller peekaboo-vision.blogspot.com Commodity Machine Learning Andreas Müller Amazon, scikit-learn
2. To Apply Machine Learning!
3. What ML can do for you
4. Hi Andy, I just received an email from the first tutorial speaker, presenting right before you, saying he's ill and won't be able to make it. I know you have already committed yourself to two presentations, but is there anyway you could increase your tutorial time slot, maybe just offer time to try out what you've taught? Otherwise I have to do some kind of modern dance interpretation of Python in data :-) -Leah Hi Andreas, I am very interested in your Machine Learning background. I work for X Recruiting who have been engaged by Z, a worldwide leading supplier of Y. We are expanding the core engineering team and we are looking for really passionate engineers who want to create their own story and help millions of people. Can we find a time for a call to chat for a few minutes about this? Thanks Classification
5. Hi Andy, I just received an email from the first tutorial speaker, presenting right before you, saying he's ill and won't be able to make it. I know you have already committed yourself to two presentations, but is there anyway you could increase your tutorial time slot, maybe just offer time to try out what you've taught? Otherwise I have to do some kind of modern dance interpretation of Python in data :-) -Leah Hi Andreas, I am very interested in your Machine Learning background. I work for X Recruiting who have been engaged by Z, a worldwide leading supplier of Y. We are expanding the core engineering team and we are looking for really passionate engineers who want to create their own story and help millions of people. Can we find a time for a call to chat for a few minutes about this? Thanks Classification
6. Classification
7. Recommendations
8. Ranking
9. Applying machine learning is easy.
10. Applying machine learning is easy. But it should be easier!
15. 500+ research papers
16. from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier() clf.fit(X_train, y_train) clf.predict(X_test)
17. from sklearn.naive_bayes import MultinomialNB from sklearn.feature_extraction.text import CountVectorizer from pipeline import make_pipeline spam_classifier = make_pipeline(CountVectorizer(), MultinomialNB()) spam_classifier.fit(email_texts, is_spam) spam_classifier.predict(new_emails) Fully Functional Spam Classifier
18. Generalized Linear Models Support Vector Machines Stochastic Gradient Descent Nearest Neighbors Gaussian Processes CCA Naive Bayes Decision Trees Ensemble methods Multiclass and multilabel algorithms Clustering Matrix Factorization Manifold Learning Mixture Models
19. “The scikit-learn tutorials / documentation is so good, one doesn't need a textbook anymore to learn a new machine learning method.”
20. This is not enough!
21. Data size Automation / Expertise needed
22. Data size Automation / Expertise needed Fits in Ram Single Machine Infinitely scalable Library One Click
23. Data size Automation / Expertise needed Fits in Ram Single Machine Infinitely scalable Library One Click Azure ML Skll
24. Why a single machine is (usually) enough
26. Smart, not Big
27. Why we need open box methods
28. Why we need black-box methods
30. predict
31. Hyperparameter Optimization Spearmint Hyperopt smac
32. From Eric Brochu, Vlad M. Cora and Nando de Freitas Bayesian Optimization
33. Why we need to scale beyond a single machine
34. Data size Automation / Expertise needed Fits in Ram Single Machine Infinitely scalable Library One Click Azure ML Skll
35. Data size Automation / Expertise needed Fits in Ram Single Machine Infinitely scalable Library One Click Azure ML Skll
36. importamueller@gmail.com @t3kcit @amueller peekaboo-vision.blogspot.com Thank you. Andreas Müller