A rough outline to whet your appetite: - Get a non-mathematical beginners introduction to machine learning - See examples of where ML is being used today - Find out how to identify where ML might be useful in your app - Find out about selecting “features” for a ML problem - Prediction.io: why it’s a good solution for developers and how to use it with Ruby - See results of a recent A/B test using prediction.io on a production application.

1. Practical Machine Learning
David Jones

2. “Field of study that gives computers the ability to
learn without being explicitly programmed”
Arthur Samuel, 1959

3. “Write a program to make this helicopter hover”

4. Pitch
Yaw
Roll

5. helicopter.rb
while helicopter.flying
if helicopter.pitch < 0
helicopter.pitchBy(0.1)
else
helicopter.pitchBy(-0.1)
end
end

6. helicopter.rb
while helicopter.flying
if helicopter.pitch < 0
helicopter.pitchBy(0.1)
else
helicopter.pitchBy(-0.1)
end
if helicopter.yaw < 0
helicopter.yawBy(0.1)
else
helicopter.yawBy(-0.1)
end
end

7. helicopter.rb
while helicopter.flying
if helicopter.pitch < 0
helicopter.pitchBy(0.1)
else
helicopter.pitchBy(-0.1)
end
if helicopter.yaw < 0
helicopter.yawBy(0.1)
else
helicopter.yawBy(-0.1)
end
if helicopter.roll < 0
helicopter.rollBy(0.1)
else
helicopter.rollBy(-0.1)
end
end

8. OK, but what if…
• it’s about to hit a tree?
• one of the main rotor blades is broken?
• power is running low?
• there is wind?

9. What if the helicopter was upside down?

10. helicopter.rb
while helicopter.flying
if helicopter.pitch < 0
helicopter.pitchBy(0.1)
else
helicopter.pitchBy(-0.1)
end
if helicopter.yaw < 0
helicopter.yawBy(0.1)
else
helicopter.yawBy(-0.1)
end
if helicopter.roll < 0
helicopter.rollBy(0.1)
else
helicopter.rollBy(-0.1)
end
end
Fail

11. Observe new
exception case
Write code to handle
exception

12. Helicopter Flying Codebase
Helicopter Flying Codebase

13. You will soon realise you can’t
explicitly handle every
exception.

14. “Field of study that gives computers the ability to
learn without being explicitly programmed”
Arthur Samuel, 1959

15. Autonomous RC Helicopter
Flown using machine learning algorithms

16. That was 8 years ago…
How good is machine learning today?

18. Germany wins

19. All 15 match outcomes predicted correctly
No “luck” here.

20. Google Search
Netflix
Sentiment Analysis
Autonomous Cars
Spam Detection
Face Detection
Siri
Priority Inbox
Medical Diagnosis Advertising
Fraud Detection
Product Recommendations
OCR
Dictation
Video Games
Finance

21. So, how does it work?

22. Collect Data
Train Model
Make Predictions

24. Two distinct algorithm types
• Supervised algorithms
• Unsupervised algorithms

25. Supervised

26. Supervised Learning
Training Data

27. estimate_sales_price.rb
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood)
price = 0
# In my area, the average house costs $200 per sqft
price_per_sqft = 200
if neighborhood == "hipsterton":
# but some areas cost a bit more
price_per_sqft = 400
elsif neighborhood == "skid row":
# and some areas cost less
price_per_sqft = 100
end
# start with a base price estimate based on how big the place is
price = price_per_sqft * sqft
# now adjust our estimate based on the number of bedrooms
if num_of_bedrooms == 0
# Studio apartments are cheap
price = price - 20000
else
# places with more bedrooms are usually
# more valuable
price = price + (num_of_bedrooms * 1000)
end
price
end

28. estimate_sales_price_ml.rb
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood)
do_some_maths(num_of_bedrooms, sqft, neighborhood)
end

29. estimate_sales_price_ml.rb
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood)
price = 0
# a little pinch of this
price += num_of_bedrooms * .841231951398213
# and a big pinch of that
price += sqft * 1231.1231231
# maybe a handful of this
price += neighborhood * 2.3242341421
# and finally, just a little extra salt for good measure
price += 201.23432095
end

30. estimate_sales_price_ml.rb
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood)
price = 0
# a little pinch of this
price += num_of_bedrooms * 1.0
# and a big pinch of that
price += sqft * 1.0
# maybe a handful of this
price += neighborhood * 1.0
# and finally, just a little extra salt for good measure
price += 1.0
end

31. …500

32. Square Feet
Number of Bedrooms

33. estimate_sales_price_ml.rb
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood)
price = 0
# a little pinch of this
price += num_of_bedrooms * .841231951398213
# and a big pinch of that
price += sqft * 1231.1231231
# maybe a handful of this
price += neighborhood * 2.3242341421
# and finally, just a little extra salt for good measure
price += 201.23432095
end

34. $300,000

35. Unsupervised

36. “Computer, tell me what’s interesting about this
data”
Training Data

37. Machine Learning
>
Explicit Programming

38. x = sqr feet
y = price

39. Selecting Features

40. Force applied, weight, colour, wind,
material, who threw it, day of week

41. Force applied, weight, colour, wind,
material, who threw it, day of week

42. Practical Machine Learning
How do I use this as a developer?

43. Algorithm Selection
How do I know what algorithm to use?

44. Algorithm Implementation
How do I implement an algorithm? Don’t.

45. Algorithm Performance
Large amounts of training data changing in
realtime

46. Hosting
How am I going to run special software
required to successfully use ML?

47. No Data?
Start logging today.

49. ML for Developers
So you don’t need to get a PHD in maths

50. Prediction.IO
• Open Source
• Deploy on your own servers or instantly on
Amazon’s Cloud
• Cheap to run
• Developer friendly API
• Easy to use admin UI

51. Prediction.IO
• Ignore the maths
• Helps you find the best algorithm for your problem
• Easily hosted and performant
• Uses scalable services such as MapReduce
and Hadoop.
• You don’t need to know how to work this stuff
though.

52. Prediction.IO
• Specialises in two use cases
• recommendations
• similarity
• more being added…

53. Product rating
Product views
Purchases

55. Selecting Features

56. Selecting Features

57. Selecting Features

58. Ruby SDK
Selecting Features

60. A/B Test Results
• 45% longer average session
• 22% increase in conversion rate
• 37% increase in average order value
• 71% increase in revenue

61. Machine Learning
• Extremely powerful at solving complex
problems
• Increasingly important for developers to
know about it
• Don’t need to know the maths to get the
benefit

62. More Information
Stanford Machine Learning
https://www.coursera.org/course/ml
Bootstrapping Machine Learning
http://www.louisdorard.com/machine-learning-book/
Machine Learning is Fun
https://medium.com/@ageitgey/machine-learning-is-fun-
80ea3ec3c471
Building The Smart Shop
http://info.resolvedigital.com/building-the-smart-spree-shop

63. David Jones
@d_jones
Questions?