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1. Data Science
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2. What is Data?
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3. What is Data?
Data is just collection of facts
23, 8000
“This is
Sparta””
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4. Data Back Then
Small
Structured
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5. Data Now
Huge
Un-structured
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6. Data Now
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7. Data Mining Use Case
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8. Data Mining Use Case
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9. Data Mining Life Cycle
Data
Acquisition
Data Pre-
Processing
Machine
Learning
Algorithm
Pattern
Evaluation
Knowledge
Representation
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10. Data Acquisition
Data from Multiple Sources Data Storage Target Data
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11. Data Pre-processing
Data Manipulation Data Visualization
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12. Machine Learning
Application of intelligent algorithms to find meaningful information from the data
Classification Regression Clustering
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13. Pattern Evaluation
Once Data Mining techniques have been applied, the results have to be evaluated
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14. Knowledge Representation
The identified patterns must be represented using simple, aesthetic graphs
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15. Data Mining Tasks–(Anomaly Detection)
Identification of unusual patterns, outliers helps in understanding the variation in data
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16. Data Mining Tasks–(Association Rule
Mining)
This method is used for finding interesting association amongst different entities
Beer Diaper Syndrome
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17. What is Machine Learning?
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18. What is Machine Learning?
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19. What is Machine Learning?
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20. What is Machine Learning?
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21. What is Machine Learning?
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22. What is Machine Learning?
Fish
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23. Categories of Machine Learning
Supervised Learning Unsupervised Learning
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24. Supervised Learning
Input Variable
Output Variable
X
Y
Y = f(X)
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25. Categories of Supervised Learning
Supervised Learning
Regression Classification
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26. Classification
Classification is the process of predicting the class of a new variable
Smoke(Yes/No) Cancer(Yes/No)
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27. Regression
This method is used to estimate the relationship between different entities
Y=f(x)
Dependent Variable Independent Variable
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28. Unsupervised Learning (Clustering)
Input Data with no
class labels
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29. Unsupervised Learning (Clustering)
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