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02 - 1. Introducing Decision Trees/04 - A quick review of machine learning basics with examples.mp4 21.3 MB03 - 2. Introducing the C5.0 Algorithm/12 - When to turn off pruning.mp4 17.2 MB03 - 2. Introducing the C5.0 Algorithm/05 - Working with the prebuilt example.mp4 16.7 MB02 - 1. Introducing Decision Trees/03 - Introducing KNIME.mp4 13.4 MB02 - 1. Introducing Decision Trees/05 - An overview of decision tree algorithms.mp4 13.1 MB05 - 4. Introducing Regression Trees/02 - The regression tree prebuilt example.mp4 12.6 MB03 - 2. Introducing the C5.0 Algorithm/02 - Understanding the entropy calculation.mp4 12.3 MB04 - 3. Introducing Classification Trees/01 - Introducing Leo Breiman and CART.mp4 12.2 MB05 - 4. Introducing Regression Trees/04 - How RT handles nominal variables.mp4 11.6 MB05 - 4. Introducing Regression Trees/05 - Ordinal variable handling.mp4 10.6 MB02 - 1. Introducing Decision Trees/02 - The pros and cons of decision trees.mp4 10.5 MB04 - 3. Introducing Classification Trees/03 - How CART handles missing data using surrogates.mp4 10.2 MB03 - 2. Introducing the C5.0 Algorithm/11 - Evaluating the accuracy of your C4.5 tree.mp4 9.7 MB05 - 4. Introducing Regression Trees/06 - Closer look at a full regression tree.mp4 9.5 MB03 - 2. Introducing the C5.0 Algorithm/06 - KNIME settings for C4.5.mp4 9.0 MB03 - 2. Introducing the C5.0 Algorithm/04 - The Give Me Some Credit data set.mp4 8.3 MB05 - 4. Introducing Regression Trees/08 - Line plot.mp4 8.3 MB04 - 3. Introducing Classification Trees/04 - Changing the settings in KNIME.mp4 8.2 MB05 - 4. Introducing Regression Trees/07 - KNIME's missing data options for regression trees.mp4 8.0 MB03 - 2. Introducing the C5.0 Algorithm/07 - How C4.5 handles nominal variables.mp4 7.8 MB
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