Machine Learning - Supervised learning

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M

Maneesha Caldera

Present about supervised learning. Mainly discussing regression and classification. Further activities are discussing how to practically apply supervised learning.

Data & Analytics

Supervised learning

Common Features
• Data set is given
• know what our correct output should look
like
• Known relationship (I/O)

Regression
• Predict results for a continuous output
• Continous function

Activity
• Map input variables to some continuous
function
– Please use given the dataset
• (/ML/Datasets/RegData.xls)

Classification
• Predict results for a discrete output
• Discrete categories

Activity
• Map input variables to some discrete
categories
– Please use given the dataset
• (/ML/Datasets/ClasificationData.xls)

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Machine Learning - Supervised learning

1. Supervised learning

2. Common Features • Data set is given • know what our correct output should look like • Known relationship (I/O)

3. Regression • Predict results for a continuous output • Continous function

4. Activity • Map input variables to some continuous function – Please use given the dataset • (/ML/Datasets/RegData.xls)

5. Classification • Predict results for a discrete output • Discrete categories

6. Activity • Map input variables to some discrete categories – Please use given the dataset • (/ML/Datasets/ClasificationData.xls)

7.

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