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.

1. @dhianadeva
MACHINE LEARNING
FOR EVERYONE
Demystifying machine learning!

2. 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

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!

11. FOR ALL

12. MASSIVE ONLINE
OPEN COURSES

13. OPEN SOURCE TOOLS

14. OPEN SOURCE
PYTHON TOOLS

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

22. 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

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

40. ALGORITHMS
Cheat sheet included!

41. CHEAT SHEET

42. CHEAT SHEET

43. ALGORITHMS PT. I
Linear Regression
Decision Trees Random Forest

44. ALGORITHMS PT. II
K-Nearest Neighbors K-Means

45. NEURAL NETWORKS

46. SELF-ORGANIZING MAPS

47. PRINCIPAL
COMPONENTS ANALYSIS

48. T-SNE

49. EVALUATION
How you doin'?

50. PRECISION AND
ACCURACY

51. 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

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_

59. PAY-AS-YOU-GO
SERVICES
Amazon Machine Learning

60. 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

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