QCon Rio - Machine Learning for Everyone

•

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Já não são mais necessários supercomputadores e times de PhDs do MIT para a criação de modelos preditivos baseados em dados. Estamos presenciando inovações em Aprendizado de Máquina que estão tornando este campo cada vez mais acessível. Esta palestra tem como objetivo desmistificar o aprendizado de máquina, através da exposição de conceitos e uso de uma série de tecnologias. Serão abordados os tipos de problemas desta área(classificação, regressão, clusterização, redução de dimensionalidade, etc.), suas as etapas (normalização, treinamento, otimização, regularização, etc.) e seus algoritmos, desde regressão linear, k-means, passando por árvores de decisão e até redes neurais, sempre aplicadas a problemas reais. Na palestra, também conheceremos ferramentas como Sckit-learn, Pandas, R, MATLAB e Amazon Machine Learning, além de uma forma para praticar e experimentar estas ideias através de competições como o Kaggle.

Dados e análise Engenharia

@dhianadeva
MACHINE LEARNING
FOR EVERYONE
Demystifying machine learning!

AGENDA
Goal:
Encourage you to start a machine learning project. Today!
● About me
● About you
● Machine Learning
● Problems
● Design
● Algorithms
● Evaluation
● Code snippets
● Pay-as-you-go
● Competitions

ABOUT ME
Electronics Engineering, Software Development and Data
Science… Why not?

MACHINE LEARNING
Learning, machine learning!

FEATURE
EXTRACTION
Item {
Feature 1
Feature 2
…
Feature N

SUPERVISED
LEARNING
458316,86.513,24.312,64.983,65.8
58317,135.493,2.204,101.966,46.5
458318,-999.0,91.803,113.007,120.
Feature VectorsItems
New item
Machine Learning
Algorithm
458316,86.513,24.312,64.983,65.8
Feature Vector
133
Expected
Label
Predictive Model
150
623
74
Labels

UNSUPERVISED
LEARNING
Items
New item
Machine Learning
Algorithm
458316,86.513,24.312,64.983,65.8
Feature Vector
Predictive Model
Better
Representation
458316,86.513,24.312,64.983,65.8
58317,135.493,2.204,101.966,46.5
458318,-999.0,91.803,113.007,120.
Feature Vectors

PROBLEMS
I've got 99 problems, but machine learning ain't one!

ALGORITHMS PT. I
Linear Regression
Decision Trees Random Forest

ALGORITHMS PT. II
K-Nearest Neighbors K-Means

CONFUSION MATRIX
Precision =
TP
Recall =
F1-score =
2 * precision * recall
precision + recall
TP + FP
TP
TP + FN
TP = True Positives
TN = True Negatives
FP = False Positives
FN = False Negatives

MATLAB 101
[x,y] = ovarian_dataset;
net = patternnet(5);
[net,tr] = train(net,x,y);
testX = x(:,tr.testInd);
testY = net(testX);

$MATLAB 201 net = patternnet(14); net.input.processFcns = {'mapminmax', 'fixunknowns', 'processpca'}; net.inputs{1}.processParams{3}.maxfrac = 0.02; net.trainFcn = 'trainlm'; net.performFcn = 'mse'; net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; [net, tr] = train(net_config, test_inputs, train_targets); outputs = net(test_inputs);$

R
library(randomForest)
raw.orig < - read.csv(file="train.txt", header=T, sep="t")
frmla = Metal ~ OTW + AirDecay + Koc
fit.rf = randomForest(frmla, data=raw)
print(fit.rf)
importance(fit.rf)

SCIKIT LEARN
dataset = pd.read_csv('Data/train.csv')
target = dataset.Activity.values
train = dataset.drop('Activity', axis=1).values
test = pd.read_csv('Data/test.csv').values
rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)
rf.fit(train, target)
predicted_probs = [x[1] for x in rf.predict_proba(test)]
importances = rf.feature_importances_

PAY-AS-YOU-GO
SERVICES
Amazon Machine Learning

AMAZON
MACHINE LEARNING
Five easy steps
1. Upload csv dataset to Amazon S3
2. Create Datasource with metadata about uploaded
dataset
3. Create ML Model with configurations for model training
4. Create Evaluation to analyse and tune model efficiency
5. Create Prediction to use trained model with new data

DATA SCIENCE
COMPETITIONS
Challenge accepted!

END TO END
TRAINTRAIN.CSV
TEST.CSV
TRAINED.DAT
RUN SOLUTION.CSV

THANK YOU
Questions?
Dhiana Deva
ddeva@thoughtworks.com

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3. ABOUT ME Electronics Engineering, Software Development and Data Science… Why not?

4. DHIANA DEVA

5. NEURALTB

6. CERN

7. NEURALRINGER

8. DJBRAZIL

9. HIGGS CHALLENGE

10. ABOUT YOU You can do it!

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13. OPEN SOURCE TOOLS

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15. PAY-AS-YOU-GO SERVICES

16. MACHINE LEARNING Learning, machine learning!

17. EXPECTATIONS

18. REALITY

19. FEATURE EXTRACTION Item { Feature 1 Feature 2 … Feature N

20. FEATURE SPACE

21. SUPERVISED LEARNING 458316,86.513,24.312,64.983,65.8 58317,135.493,2.204,101.966,46.5 458318,-999.0,91.803,113.007,120. Feature VectorsItems New item Machine Learning Algorithm 458316,86.513,24.312,64.983,65.8 Feature Vector 133 Expected Label Predictive Model 150 623 74 Labels

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23. MODELS

24. BIOLOGICAL MOTIVATION

25. PROBLEMS I've got 99 problems, but machine learning ain't one!

26. CLASSIFICATION ? A B

27. CLASSIFICATION

28. REGRESSION ? 8 15 7 1 11 13 6 3

29. REGRESSION

30. CLUSTERING

31. CLUSTERING

32. DIMENSIONALITY REDUCTION

33. DIMENSIONALITY REDUCTION

34. DESIGN DECISIONS 1, 2 steps!

35. NORMALIZATION ● min-max ● z-score

36. TRAINING

37. REGULARIZATION

38. RELEVANCE ANALYSIS

39. CROSS VALIDATION

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41. CHEAT SHEET

42. CHEAT SHEET

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45. NEURAL NETWORKS

46. SELF-ORGANIZING MAPS

47. PRINCIPAL COMPONENTS ANALYSIS

48. T-SNE

49. EVALUATION How you doin'?

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52. ROC CURVE

53. A/B TESTS

54. Code Snippets "Hello, Machine Learning"

55. MATLAB 101 [x,y] = ovarian_dataset; net = patternnet(5); [net,tr] = train(net,x,y); testX = x(:,tr.testInd); testY = net(testX);

56. MATLAB 201 net = patternnet(14); net.input.processFcns = {'mapminmax', 'fixunknowns', 'processpca'}; net.inputs{1}.processParams{3}.maxfrac = 0.02; net.trainFcn = 'trainlm'; net.performFcn = 'mse'; net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; [net, tr] = train(net_config, test_inputs, train_targets); outputs = net(test_inputs);

57. R library(randomForest) raw.orig < - read.csv(file="train.txt", header=T, sep="t") frmla = Metal ~ OTW + AirDecay + Koc fit.rf = randomForest(frmla, data=raw) print(fit.rf) importance(fit.rf)

58. SCIKIT LEARN dataset = pd.read_csv('Data/train.csv') target = dataset.Activity.values train = dataset.drop('Activity', axis=1).values test = pd.read_csv('Data/test.csv').values rf = RandomForestClassifier(n_estimators=100, n_jobs=-1) rf.fit(train, target) predicted_probs = [x[1] for x in rf.predict_proba(test)] importances = rf.feature_importances_

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61. EVALUATION

62. SDKs

63. DATA SCIENCE COMPETITIONS Challenge accepted!

64. KAGGLE

65. SPONSORED

66. END TO END TRAINTRAIN.CSV TEST.CSV TRAINED.DAT RUN SOLUTION.CSV

67. THANK YOU Questions? Dhiana Deva ddeva@thoughtworks.com