MachineLearning for dummies with Python Have you heard that Machine Learning is the next big thing? Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills? If your response to both questions is 'Yes', we are in the same position. Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.

1. Machine Learning
for dummies
with Python
PYBCN
Javier Arias
@javier_arilos

2. One day in your life
June 2016

3. One day in your life
Time to leave the office

4. One day in your life
Tesla autopilot

5. One day in your life

6. One day in your life
Playing music

7. One day in your life
Your photos organized

8. One day in your life
Machine Learning is here,
it is everywhere and
it is going to stay

9. About this presentation
Why Machine Learning (ML) matters
A journey on Machine Learning
Some ML technologies and resources
Some basic ML concepts, with code sample

10. Machine Learning is the next big thing

11. Are machines already intelligent?

12. Image-net challenge
2015: machines outperform people

13. Chess
1997: Deepblue defeats Kasparov

14. Game of Go
2016: AlphaGo wins world champion Lee Sedol

16. The journey

17. Learning about ML
MOOC - Massive Open Online Courses
Contents by the best universities and companies
Some superstar teachers
Udacity, Coursera, EdX

18. Udacity - Intro to Machine Learning
Pattern Recognition for Fun and Profit
- Very well organized contents
- Python + sklearn
- Sebastian Thrun
- Free
- At your own pace

19. Udacity - Intro to Machine Learning
Pattern Recognition for Fun and Profit

20. Udacity - Intro to Machine Learning
Pattern Recognition for Fun and Profit

21. Udacity - Deep Learning
Take machine Learning to the next level
ML branch based on algorithms that use multiple processing layers
● By Google
● Python and Tensorflow
● No wine for the moment :-(

22. What is Machine Learning?

23. Solving a complex problem
somethingfeatures
(data)
prediction

24. First approach, programming
tell the computer what to do in very tiny steps

25. … represented primarily
as if-then rules ...
Artificial Intelligence - expert systems

26. Machine Learning
show the computer some real world data and let it
learn from it

27. Machine Learning, implications
We can train computers to do things we do not
know how to do

29. ML example: character recognition
Not-MNIST dataset
Thousands of 28x28 grayscale images with labels

30. features x 1000s labels x 1000s
F G F J
ML step 1: get samples (training data)

31. ML step 2: choose an algorithm
Linear regression
Support Vector Mach.
k-Means
Decision Trees
Random Forests
Neural networks
Convolutional NN
Naive Bayes

32. ML step 3: train your algorithm
features x 1000s labels x 1000sML algorithm
F G F J

33. ML, last step: getting predictions
ML algorithmfeatures
(data)
prediction
D

34. Tricky Question
How good are our
predictions?

35. The Tools

36. The Tools
Python sklearn tensorflow

37. The Tools: Python
● Opensource
● Expressive
● Interpreted, dynamically typed
● Widely used many different problems
● Batteries included: Notebook, Libraries

38. The Tools: sklearn
● Opensource, Python
● Wonderful documentation
● Support to full ML lifecycle:
○ Feauture engineering
○ Algorithms
○ Validation
○ Datasets

39. The Tools: TensorFlow
● Opensource, Python
● Deep Learning
● Data flow graphs.
○ Nodes: mathematical operations
○ Edges: Tensors, multidimensional arrays

41. A summary of ML process
● Get features (with labels)
● Choose and configure an algorithm
● Train your algorithm
● Do predictions
● Validate your results

42. train your model
tr_ds, _, tr_lbl, _ = train_test_split(dataset, labels,
train_size=size,
random_state=17)
clf = LogisticRegression()
clf.fit(tr_ds, tr_lbl) # fit with train dataset and train labels
train_ds test_ds
dataset

43. make predictions
pred = clf.predict(test_dataset)
How good are our predictions?

44. accuracy
test_predicions = clf.predict(test_dataset)
acc = accuracy_score(test_labels, test_predictions)

45. 89% accuracy

46. Improving prediction results
Training data
Algorithm + config

47. Is Machine Learning black magic?

48. Machine Learning is mathematics

49. Linear regression - predict house prices

50. Linear regression

51. Linear regression
price = m·size + n

52. Linear regression - starting training

53. Linear regression - training iteration

54. Linear regression - error minimized

55. SVM classification example

56. ML finds boundaries in your data

58. 89% accuracy… It can never be too much.

59. First trained algorithm - 30% accuracy

60. 75% accuracy - should we aim for 100%?

61. 100% accuracy, now we are talking!

62. overfitting

63. Let’s recap

71. Questions?