This is an introduction to Machine Learning and how it fits into Data Science as well as the larger tech space in general.

2. Machine Learning
Frank D. Evans
Thunder Plains 2016

9. A Data Scientist is
a data analyst that
lives in California

10. A Data Scientist is
a better mathematician than
any of the programmers,
and a better programmer than
any of the mathematicians.

12. OSEMN

13. OSEMN
Obtain

14. OSEMN
Scrub

15. OSEMN
Explore

16. OSEMN
Model

17. OSEMN
Interpret

22. T
O
O
L
S

23. T
O
O
L
S

24. T
O
O
L
S

25. T
O
O
L
S

27. Data is huge.
People are expensive.
Computation is cheap.

28. Use applied statistics
to let the computers
program themselves.

29. Types

30. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."

31. Types
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."

32. Types
Reinforcement
"I want to start with
what I know now,
and be able to learn
new things as new
data comes along."

33. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."
Reinforcement
"I want to start with
what I know now,
and be able to learn
new things as new
data comes along."

34. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."
Reinforcement
"I want to start with
what I know now,
and be able to learn
new things as new
data comes along."

35. Regression vs Classification
Supervised Learning

36. Regression
Use continuous data to
make a model that
predicts where new
data will fit.

37. Classification
Label data into
"buckets", and make
predictions on which
bucket a new data
point will fall into.

38. Unsupervised Learning

39. Reinforcement Learning

40. Reinforcement Learning

41. Deep Learning

42. Deep Learning
generalization, not memorization

43. generalization, not memorization

44. generalization, not memorization
Overfitting

45. Domains
Tabular

46. Domains
Text

47. Domains
Graph

48. Domains
Viz

49. You’re not trying to learn about
the data, you’re trying to use the
data to learn about the world.

52. exaptive.com/blog
Frank D. Evans
@frankdevans
@exaptive
slideshare.net/frankdevans