In this session, Sinan introduces Supervised Learning, help you build an intuition about it, and walk you through an example with Python using scikit-learn. You'll see it is pretty straightforward, and you might find room to apply Supervised Learning in your current or next project! Presented by: Sinan Taifour, CTO at CashBasha.com Session video recording is on: https://youtu.be/nfbBrPk3EiQ

1. An Introduction To
Supervised Learning

2. Disclaimer!

3. Artificial Intelligence
Machine Learning …
Supervised Learning ...

4. Learning
Algorithm
A( , “Sports”)
B( , “Politics”)
C( , “Business”)
Model
(Function)
“Business”
P
fit() predict()
…

5. Our
Learning
Algorithm
Our
Model
(Function)
“Clean”
(10, “Not Clean”)
(120, “Clean”)
(30, “Not Clean”)
...
105
Minutes in
Washing Machine
Cleanliness

6. Our
Learning
Algorithm
Our
Model
(Function)
1
(10, 0)
(120, 1)
(30, 0)
...
105
Minutes in
Washing Machine
Cleanliness

7. Our
Learning
Algorithm
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

8. Our
Learning
Algorithm
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

9. 1. Sort data
2. Scan from left
looking for change
in label
3. Set value of “a”
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

10. 1. Sort data
2. Scan from left
looking for change
in label
3. Set value of “a”
y
x(10, 0)
(120, 1)
(30, 0)
...
fit() predict()

11. Laundry Cleanliness (Data)

12. Laundry Cleanliness (Data & Model)

13. Satisfaction From Allowance (Data)

14. Satisfaction From Allowance (Data & Model)

15. Satisfaction From Allowance (Data & Model)

16. Number of Facebook Friends till Platform is
Attractive (Data)

17. Number of Facebook Friends till Platform is
Attractive (Data & Model)

18. Average Temperature for Successful Grape
Growing (Data)

19. Average Temperature for Successful Grape
Growing (Data & Model)
✗
✓

20. Learning Algorithms
Have Limited Expressibility

21. Learning
Algorithm
A( , “Sports”)
B( , “Politics”)
C( , “Business”)
Model
(Function)
“Business”, “Politics”, “Business”, ...
A
fit() predict()
…
Evaluation
B C, , , ...
Evaluate

22. Training vs Testing
B “Politics”
C “Business”
D “Business”
E “Sports”
F “Politics”
G “Politics”
H “Business”
A “Sports”

23. Training vs Testing
B “Politics”
C “Business”
D “Business”
E “Sports”
F “Politics”
G “Politics”
H “Business”
A “Sports”
fit()
predict()
“Sports”
“Politics”
“Business”
Evaluate
0.67

24. Over-fitting
(On to the whiteboard!)

25. Learning
Algorithm
Model
(Function)
1
(10, 0)
(120, 1)
(30, 0)
...
105
Minutes in
Washing Machine
Cleanliness

26. Learning
Algorithm
Model
(Function)
1
([10, 1.4], 0)
([120, 3], 1)
([30, 0.5], 1)
...
[105, 1.6]
Minutes in
Washing Machine
Cleanliness
Weight of Load

27. 2D-Laundry Cleanliness (Data)

28. 2D-Laundry Cleanliness
(Linear Model)

29. 2D-Laundry Cleanliness
(QDA Model)

30. 2D-Laundry Cleanliness
(Decision Tree Model)

31. 2D-Laundry Cleanliness
(Random Forest Model)

32. 2D-Laundry Cleanliness
(K-Nearest Neighbors Model)

33. 2D-Laundry Cleanliness
(RBF SVM Model)

34. Learning
Algorithm
Model
(Function)
1
([10, 1.4,40, “Samsung”, ...], 0)
([120, 3, 50, “AEG”, ...], 1)
([30, 0.5, 30, “Siemens”, ...], 1)
...
[105, 1.6, 40, “AEG”, ...]
Minutes in
Washing Machine
Cleanliness
Weight of Load
Temperature
Washing Machine
Brand

35. Feature Extraction
A [124, 2, 40, 30, 12, ...]
Feature
Extractor

36. And now,
a demo with scikit-learn.