The document discusses supervised machine learning. It defines supervised machine learning and provides examples of regression, classification, and ranking problems. It also includes examples of datasets with features and target values for classification and regression problems. Machine learning algorithms are trained on these labeled examples to learn a function that maps new examples to output labels.

1. Supervised
Machine Learning
DataTalks.Club
Machine Learning Zoomcamp
Session #1.3

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Session #1.2: Plan
● Formal definition of supervised machine learning
● Examples: regression, classification, ranking

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Supervised Machine Learning
󰠅
🤖🤖🤖
🤖🤖🤖
$1.1k

4. DataTalks.Club — mlzoomcamp.com — @Al_Grigor
Supervised Machine Learning
󰠅
🤖🤖🤖
🤖🤖🤖
spam

5. DataTalks.Club — mlzoomcamp.com — @Al_Grigor
[1, 1, 0, 0, 1, 1] 1
[0, 0, 0, 1, 0, 1] 0
[1, 1, 1, 0, 1, 0] 1
[1, 0, 0, 0, 0, 1] 1
[0, 0, 0, 1, 1, 0] 0
[1, 0, 1, 0, 1, 1] 0
Features
(data)
Target
(desired output)

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A bit of math

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[0, 0, 0, 1, 0, 1]
[0, 0, 0, 1, 1, 0]
[1, 0, 1, 0, 1, 1]
[1, 1, 1, 0, 1, 0]
[1, 0, 0, 0, 0, 1]
[1, 1, 0, 0, 1, 1]
Features
(data)
Predictions
(output)
0.93
0.48
0.19
0.32
0.01
0.94

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Supervised Machine Learning
$50k
Regression:

9. DataTalks.Club — mlzoomcamp.com — @Al_Grigor
Supervised Machine Learning
$50k
Regression:
$1m
Regression: 🏠

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Supervised Machine Learning
car
Classification:

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Supervised Machine Learning
spam
Classification:
car
Classification:
✉

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Classification
cat
dog
car
Multiclass:

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Classification
spam
not spam
Binary:
cat
dog
car
Multiclass:
✉

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Ranking

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17. DataTalks.Club — mlzoomcamp.com — @Al_Grigor Summary

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Process: CRISP-DM