This document provides an overview of machine learning concepts and processes. It discusses types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. It also outlines the typical machine learning process of data collection/preparation, modeling, training models, evaluating results, and improving models. Specific examples discussed include using machine learning for email marketing conversion prediction, rhythmic gymnastics image classification, and recommendation systems.

1. Machine Learning
Bayram Annakov, Empatika Open

3. Types of ML

4. Supervised
machine learning

5. Supervised learning
x
o
x
xx
x
x
o
o
o
o
o

6. Classification

7. Unsupervised
learning

8. Unsupervised
learning
o
o
o
o
o
o
o
o
o
o
o
o
X1
X2

9. Users clustering

10. Process

11. Baby first steps
• GOAL: better purchase conversion from Trial Emails
• Knowledge: internal Empatika Open
• Whats next?
• Load new data & apply

12. First results - I’m genius!
KNN: 97% score on test set

13. … & first disappointment
Too many Negative - unbalanced dataset
Balanced: 64%
Better do something: email conversion 2 times less

14. Start from scratch
+
How:
1. Small dataset (own)
• Time
• Value
2. Balanced
3. Don’t hurry
lots of answers

15. Process
Data
Model 1 Model 2 Model N………
Results 1 Results 2 Results N
Reducing
features
size
Scaling
…
other data
stuffBest result
Train model
(parameters)
………

16. Results
Test dataset
New dataset
30% better
7% better
Email conversion 2 times better Vs. previous model
416 different inputs

17. Next level
• Features
• Volume
• Understand Model parameters
• Train model harder (24/7)
• Whole picture: not only 1 score, but Precision,
Recall, f1-score, etc.

18. Lessons & Knowledge source
• Think about features (valuable VS lots VS less) balance
• Models are sensitive to different data
• Model tuning is important, but long road
• Sources:
• O’REILLY: Introduction to Machine Learning with Python
• scikit-learn.org
• Github

19. Be patient

20. Process
Data collection & preparation
Modeling
Training
Evaluation

21. Data preparation!!!

22. Tasks

23. Images
classification

24. Rhythmic Gymnastics

25. Rhythmic Gymnastics

26. Approach
• Collect data
Simple iPhone app that helps draw and export
• Prepare data
Image = Grid. Each cell = 1 (black) or 0 (white)
Convert Grid to Line
Image = 000100011000011100011…
• Train + Analyze
Until satisfied with the score

28. Prepare data
import skimage
import numpy

29. Train and Analyze
1. K-neighbors 78%
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=1)
clf.fit(x_train, y_train)
clf.score(x_test, y_test)

30. Train and Analyze
2. K-neighbors + PCA 81%
from sklearn.decomposition import PCA
pca = PCA(n_components=40, whiten=True)
pca.fit(x_train)
x_train_pca = pca.transform(x_train)
x_test_pca = pca.transform(x_test)
//repeat KNN

31. May be someone has
already solved it?

32. MNIST
http://yann.lecun.com/exdb/mnist/

33. 3. SVM 90%
from sklearn import svm
classifier = svm.SVC(gamma=0.001)
classifier.fit(x_train, y_train)
predicted = classifier.predict(x_test)

34. Neural networks?

35. Neuron

36. Perceptron

37. Multi-layered
(deep)

38. Problems with
images
Too big vectors
(200x200x3 =
120,000)
Pixel position
matters

39. Convolution

40. Pooling (sub-
sampling)

41. CNN

43. Object recognition

45. ImageNet

46. Faces recognition

47. Eigenfaces

48. LFW

49. Recommendation
systems

52. NLP

53. Bag-of-words

54. Data is key

55. Competitive
Advantage?

56. Costs

57. Why?

58. CPU vs GPU

59. Opportunities

61. Better than
Google?

62. Attributes
Proprietary data sets
Domain-specific tasks
Domain-specific knowledge

63. So,

64. Useful links
“The Master Algorithm”
Andrew Ng “AI is new electricity”
fast.ai course
“Introduction to ML with Python”
“Python Machine Learning”

65. one more thing…

66. Please donate any sum to any fund

68. Plans
3 universities in Paris
Crowdfunding
Platform

69. Not only academics

70. New Tech

71. How you can help?
Finances
Introductions
Ideas
Expertise
Media
Tech
even frequent flyer miles :)

72. Thanks
Lucy Evstratova
+79165884397
Unicore.pro
AlfaBank
4154 8120 0093 9516
Sberbank
4276 3800 1234 3302

73. Ачворвоы выовпывп ывп ыврп