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[ FreeCourseWeb com ] Linkedin - Machine Learning and AI Foundations - Decision Trees with KNIME
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01 - Introduction
01 - The basics of decision trees.mp4
01 - The basics of decision trees.srt
02 - What you should know.mp4
02 - What you should know.srt
03 - How to use the practice files.mp4
03 - How to use the practice files.srt
02 - 1. Introducing Decision Trees
01 - What is a decision tree.mp4
01 - What is a decision tree.srt
02 - The pros and cons of decision trees.mp4
02 - The pros and cons of decision trees.srt
03 - Introducing KNIME.mp4
03 - Introducing KNIME.srt
04 - A quick review of machine learning basics with examples.mp4
04 - A quick review of machine learning basics with examples.srt
05 - An overview of decision tree algorithms.mp4
05 - An overview of decision tree algorithms.srt
03 - 2. Introducing the C5.0 Algorithm
01 - Ross Quinlan, ID3, C4.5, and C5.0.mp4
01 - Ross Quinlan, ID3, C4.5, and C5.0.srt
02 - Understanding the entropy calculation.mp4
02 - Understanding the entropy calculation.srt
03 - How C4.5 handles missing data.mp4
03 - How C4.5 handles missing data.srt
04 - The Give Me Some Credit data set.mp4
04 - The Give Me Some Credit data set.srt
05 - Working with the prebuilt example.mp4
05 - Working with the prebuilt example.srt
06 - KNIME settings for C4.5.mp4
06 - KNIME settings for C4.5.srt
07 - How C4.5 handles nominal variables.mp4
07 - How C4.5 handles nominal variables.srt
08 - How C4.5 handles continuous variables.mp4
08 - How C4.5 handles continuous variables.srt
09 - Equal size sampling.mp4
09 - Equal size sampling.srt
10 - A quick look at the complete C4.5 tree.mp4
10 - A quick look at the complete C4.5 tree.srt
11 - Evaluating the accuracy of your C4.5 tree.mp4
11 - Evaluating the accuracy of your C4.5 tree.srt
12 - When to turn off pruning.mp4
12 - When to turn off pruning.srt
04 - 3. Introducing Classification Trees
01 - Introducing Leo Breiman and CART.mp4
01 - Introducing Leo Breiman and CART.srt
02 - What is the Gini coefficient.mp4
02 - What is the Gini coefficient.srt
03 - How CART handles missing data using surrogates.mp4
03 - How CART handles missing data using surrogates.srt
04 - Changing the settings in KNIME.mp4
04 - Changing the settings in KNIME.srt
05 - How CART handles nominal variables.mp4
05 - How CART handles nominal variables.srt
06 - A quick look at the complete CART tree.mp4
06 - A quick look at the complete CART tree.srt
07 - Evaluating the accuracy of your CART tree.mp4
07 - Evaluating the accuracy of your CART tree.srt
05 - 4. Introducing Regression Trees
01 - MPG data set.mp4
01 - MPG data set.srt
02 - The regression tree prebuilt example.mp4
02 - The regression tree prebuilt example.srt
03 - The math behind regression trees.mp4
03 - The math behind regression trees.srt
04 - How RT handles nominal variables.mp4
04 - How RT handles nominal variables.srt
05 - Ordinal variable handling.mp4
05 - Ordinal variable handling.srt
06 - Closer look at a full regression tree.mp4
06 - Closer look at a full regression tree.srt
07 - KNIME's missing data options for regression trees.mp4
07 - KNIME's missing data options for regression trees.srt
08 - Line plot.mp4
08 - Line plot.srt
09 - Accuracy.mp4
09 - Accuracy.srt
06 - Conclusion
01 - Next steps.mp4
01 - Next steps.srt
Bonus Resources.txt
Ex_Files_ML_and_AI_Foundations_Decision_Trees_KNIME
Exercise Files
Chapter_2_Example_for_Learning_a_Decision_Tree.knwf
Chapter_3_Example_for_Learning_a_Decision_Tree.knwf
Chapter_4_Example_for_Learning_a_Decision_Tree.knwf
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