We live in the world where we would like everything to be available in an instant. This is why we have instant noodles, instant coffee, and more things available in a flick of a second. In business and other activities, we also want things to be readily available the time we need them. This is why artificial intelligence has been developing to cope with the demand of times. Through machine learning we are able to predict future performances of a certain business, an athlete, stocks, sites and many more. Machine learning is an amazing method of data analysis that automates model building or algorithm. Here is the good thing about this: your past data can be updated once new information is fed. In short, they are able to adapt independently.

1. How to use
Machine Learning
effectively

2. So many
questions may
come to mind…

4. These must be
answered.

5. So you consult
many people.

7. With machine
learning, it can be
made easy.

9. Why is this so?

10. It uses historical
data to make
predictions.

12. It can be broken
down into five
basic steps.

13. One

14. Choose data.

16. What kind of
data are you
going to input?

17. Stocks

18. Sales

19. Scores

20. These data
depend upon
your need.

21. Second

22. Massage or
clean data

24. Here, you detect
and correct
corrupt data.

25. You identify
incomplete,
incorrect, …

26. inaccurate or
irrelevant parts of
the data.

27. Then, replacing,
modifying, or
deleting them.

28. Steps 1 and 2
area bout data
collection.

29. This makes sure
that…

30. you make the
right data set.

32. Once you have
your structured
data, …

33. you can move
on to the next
step.

35. Third

36. Split into training
or data set

37. This is an important
part of evaluating
data.

38. In training set,
…

39. you apply the
model you
design.

40. You assess the
strength of a
predictive
relationship.

41. In test set, you
test the trained
model.

43. You are now
ready for the
next step.

44. Fourth

45. Apply
algorithm

46. You apply this
algorithm on the
trained…

47. that you have
extracted in
step 3.

48. This is the most
important part.

49. As you need to
understand this:

50. What kind of
algorithm you
need to apply…

51. so you can
achieve the kind
of data you need.

52. Once you have
your trained
model,…

53. you need to find
the answer to this:

54. Is it good
enough?

55. So let’s talk
about the last
step.

56. Lastly

57. Score &
Evaluate

58. What will you
score and
evaluate?

59. Score your
trained model.

60. Evaluate its
performance.

61. Thank you for
watching!