Linkedin Machine Learning and AI Foundations Decision Trees with KNIME

Linkedin Machine Learning and AI Foundations Decision Trees with KNIME
     Get Bonus Downloads Here.url -


     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 -


     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 -


     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 -


     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 -


     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 -


     01 - Next steps.mp4 -


     01 - Next steps.srt -


     Bonus Resources.txt -


     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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