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![How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/predictiveappsforstartups-160311194046/85/Predictive-apps-for-startups-39-320.jpg)
![How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])
WAT?](https://image.slidesharecdn.com/predictiveappsforstartups-160311194046/85/Predictive-apps-for-startups-40-320.jpg)
![Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/predictiveappsforstartups-160311194046/85/Predictive-apps-for-startups-43-320.jpg)
![Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/predictiveappsforstartups-160311194046/85/Predictive-apps-for-startups-44-320.jpg)
![AutoML Scikit
import autosklearn
model = autosklearn.AutoSklearnClassifier()
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/predictiveappsforstartups-160311194046/85/Predictive-apps-for-startups-45-320.jpg)
Uploaded byLouis Dorard
Predictive apps for startups
This document discusses predictive apps for startups using machine learning. It provides examples of everyday use cases like real estate price prediction and email spam detection. It explains that machine learning works by training a model on data and then using the model to make predictions on new data. The document also discusses how to make machine learning more accessible through cloud platforms and APIs, and how automation tools can help simplify machine learning tasks like model tuning and algorithm selection.
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Predictive apps for startups
- 1. Predictive Apps forStartups @louisdorard #MLVLC - 11 March 2016
- 2.
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- 11.
- 12. How does itwork?
- 13. Data + MachineLearning
- 15. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 16. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 18. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 19. ML is aset of AI techniques where “intelligence” is built from examples
- 21. “Weak AI” vs.“Strong AI”
- 22. 22 Everyday use cases •Real-estate • Spam • Priority inbox • Crowd prediction property price email spam indicator email importance indicator location & context #people Zillow Gmail Gmail Tranquilien
- 23. Making Machine Learningaccessible with cloud platforms
- 25. HTML / CSS/ JavaScript
- 26. HTML / CSS/ JavaScript
- 27.
- 30. The two phasesof ML • TRAIN a model • PREDICT with a model 30 Machine Learning APIs
- 31. The two methodsof ML Application Programming Interfaces (here in Python) • model = create_model(‘training.csv’) • predicted_output, confidence = create_prediction(model, new_input) 31 Machine Learning APIs
- 32. The two methodsof ML Application Programming Interfaces (here in Python) • model = create_model(‘training.csv’) • predicted_output, confidence = create_prediction(model, new_input) 32 Machine Learning APIs
- 33. Example request toBigML API $ curl https://bigml.io/dev/model?$BIGML_AUTH -X POST -H "content-type: application/json" -d '{"dataset": "dataset/50ca447b3b56356ae0000029"}'
- 35. • Classification problem •Features: • Text of email • Sender in address book? • How often do I reply? • How quickly do I reply? • Demo 35 Priority detection
- 37. • VM withJupyter notebooks (Python & Bash) • API wrappers preinstalled: BigML & Google Pred • Notebook for easy setup of credentials • Scikit-learn and Pandas preinstalled • Open source VM provisioning script & notebooks • Search public Snaps on terminal.com:“machine learning” 37 Getting started
- 38. Making Machine Learningeasier
- 39. How was itbefore? from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 40. How was itbefore? from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) WAT?
- 41.
- 42. • Spearmint:“Bayesian optimization”fortuning parameters → Whetlab → Twitter • Auto-sklearn:“automated machine learning toolkit and drop- in replacement for a scikit-learn estimator” 42 Open Source AutoML libraries
- 43. Scikit from sklearn importsvm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 44. Scikit from sklearn importsvm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 45. AutoML Scikit import autosklearn model= autosklearn.AutoSklearnClassifier() from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 46. • Algorithm selection…AutoML • Scaling… Azure ML or Yhat (Greg at PAPIs Connect) • “Automating ML workflows: a report from the trenches”— Jose A. Ortega Ruiz 46 Automatization
- 47. Making Deep Learningaccessible
- 48. • Classification problem •Input is an image = pixel values • Deep Learning! (with Vincent) 48 Image categorization
- 49. 49 Machine Learning forperson detection pixel1 pixel2 pixel3 person? 102 0 255 Yes 35 41 209 No … … … …
- 50. • Neural network: •Layers • Neurons of one layer connected to neurons of next layer • Each neuron receives signals from previous layer and sends new signal to next layer • New signal based on linear combination of signals received • “Deep”-> more than 3 layers 50 Deep Learning
- 51. 51 Deep Learning foranimal detection
- 52. 52 Deep Learning foranimal detection pixel1 pixel2 pixel3 cat dog 1st layer value=(102, 0, 255) Last layer value=(0.1, 0.7, 0.4) Output value=(0.8, 0.3) => there’s probably a cat!
- 53. 53 Deep Learning foranimal detection pixel1 pixel2 pixel3 cat dog 1st layer value=(4, 166, 23) Last layer value=(0.1, 0.7, 0.4) Output value=(0.1, 0.2) => probably no animal here
- 54. pixel1 pixel2 pixel3 cat dog 1st layer value=(102, 0,255) Output value=(0.8, 0.3) => there’s probably a cat! Last layer value=(0.1, 0.7, 0.4)
- 55. 55 Machine Learning forperson detection pixel1 pixel2 pixel3 person? 102 0 255 Yes 35 41 209 No … … … … • Use network for animal detection until last layer • Replace images with“smart”representation given by last layer neuron1 neuron2 neuron3 person? 0.1 0.2 0.5 Yes 0.8 0.3 0.8 No … … … …
- 57. • Artificial Intelligencefor Business and Society • Next Monday & Tuesday • papis.io/connect • Discount for 24 hours only! 57 PAPIs Connect