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Supervised learning - Classification
Decision trees -- the C4.5 algorithm
Decision trees -- the C4.5 algorithm
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Day 4 - 1 - Decision trees - C45.ppt
za ogled datoteke.
◄ What have I learned?
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Announcements
Discussion
Re-training program (tentative)
Re-training agenda (tentative)
The CRISP-DM standard
What have I learned?
Input concepts
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Visualization
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Introduction to statistics
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Data preparation
What have I learned?
Tutorial #1: The Iris data set -- selected formats
Exercise #1: A selected UCI ML data set in ARFF format
Tutorial #2: EDA and data preparation
Exercise #2: EDA and preparation on UCI ML Labor data set
Corpora
POS tagging / corpus tagging
Terminology
Text mining
Sketch engine workshop
Simple classification methods
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Decision trees -- introduction
What have I learned?
What have I learned?
Decision rules -- the covering algorithm
What have I learned?
Tutorial #3: Classification basics and Decision trees
Exercise #3: Classification algorithms -- basics
Tutorial #4: Classification -- the C4.5 algorithm
Exercise #4: Using J4.8 to model sample data
Tutorial #5: Decision rules -- the covering algorithm
Exercise #5: Using the PRISM algorithm to learn decision rules
Evaluation
Tutorial #6: Evaluation
Exercise #6: Comparing different evaluation strategies and measures
Regression and nearest neighbors
Tutorial #7: Regression and nearest neighbors
Exercise #7: Linear regression, the kNN algorithm
What have I learned? ►