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Checked by:
Category:
Language:
English
Total Size:
311.0 MB
Info Hash:
B68A5C879EF9D3F50DC9D0EC319F5BA21516067B
Added By:
Added:
Oct. 23, 2023, 11:20 a.m.
Stats:
|
(Last updated: May 17, 2025, 1:07 a.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 183 bytes |
| 01 - The basics of decision trees.mp4 | 7.2 MB |
| 01 - The basics of decision trees.srt | 2.1 KB |
| 02 - What you should know.mp4 | 2.0 MB |
| 02 - What you should know.srt | 1.6 KB |
| 03 - How to use the practice files.mp4 | 4.5 MB |
| 03 - How to use the practice files.srt | 2.2 KB |
| 01 - What is a decision tree.mp4 | 7.2 MB |
| 01 - What is a decision tree.srt | 4.9 KB |
| 02 - The pros and cons of decision trees.mp4 | 10.1 MB |
| 02 - The pros and cons of decision trees.srt | 8.1 KB |
| 03 - Introducing KNIME.mp4 | 12.8 MB |
| 03 - Introducing KNIME.srt | 6.0 KB |
| 04 - A quick review of machine learning basics with examples.mp4 | 20.3 MB |
| 04 - A quick review of machine learning basics with examples.srt | 10.4 KB |
| 05 - An overview of decision tree algorithms.mp4 | 12.5 MB |
| 05 - An overview of decision tree algorithms.srt | 5.8 KB |
| 01 - Ross Quinlan, ID3, C4.5, and C5.0.mp4 | 5.7 MB |
| 01 - Ross Quinlan, ID3, C4.5, and C5.0.srt | 3.6 KB |
| 02 - Understanding the entropy calculation.mp4 | 11.7 MB |
| 02 - Understanding the entropy calculation.srt | 9.1 KB |
| 03 - How C4.5 handles missing data.mp4 | 6.0 MB |
| 03 - How C4.5 handles missing data.srt | 4.4 KB |
| 04 - The Give Me Some Credit data set.mp4 | 7.9 MB |
| 04 - The Give Me Some Credit data set.srt | 4.6 KB |
| 05 - Working with the prebuilt example.mp4 | 15.9 MB |
| 05 - Working with the prebuilt example.srt | 8.8 KB |
| 06 - KNIME settings for C4.5.mp4 | 8.6 MB |
| 06 - KNIME settings for C4.5.srt | 4.9 KB |
| 07 - How C4.5 handles nominal variables.mp4 | 7.4 MB |
| 07 - How C4.5 handles nominal variables.srt | 3.6 KB |
| 08 - How C4.5 handles continuous variables.mp4 | 4.2 MB |
| 08 - How C4.5 handles continuous variables.srt | 2.3 KB |
| 09 - Equal size sampling.mp4 | 6.4 MB |
| 09 - Equal size sampling.srt | 3.3 KB |
| 10 - A quick look at the complete C4.5 tree.mp4 | 6.4 MB |
| 10 - A quick look at the complete C4.5 tree.srt | 4.3 KB |
| 11 - Evaluating the accuracy of your C4.5 tree.mp4 | 9.3 MB |
| 11 - Evaluating the accuracy of your C4.5 tree.srt | 4.4 KB |
| 12 - When to turn off pruning.mp4 | 16.4 MB |
| 12 - When to turn off pruning.srt | 8.6 KB |
| 01 - Introducing Leo Breiman and CART.mp4 | 11.6 MB |
| 01 - Introducing Leo Breiman and CART.srt | 5.9 KB |
| 02 - What is the Gini coefficient.mp4 | 7.0 MB |
| 02 - What is the Gini coefficient.srt | 4.0 KB |
| 03 - How CART handles missing data using surrogates.mp4 | 9.8 MB |
| 03 - How CART handles missing data using surrogates.srt | 8.0 KB |
| 04 - Changing the settings in KNIME.mp4 | 7.8 MB |
| 04 - Changing the settings in KNIME.srt | 4.5 KB |
| 05 - How CART handles nominal variables.mp4 | 4.6 MB |
| 05 - How CART handles nominal variables.srt | 2.6 KB |
| 06 - A quick look at the complete CART tree.mp4 | 7.2 MB |
| 06 - A quick look at the complete CART tree.srt | 3.6 KB |
| 07 - Evaluating the accuracy of your CART tree.mp4 | 3.4 MB |
| 07 - Evaluating the accuracy of your CART tree.srt | 2.0 KB |
| 01 - MPG data set.mp4 | 4.5 MB |
| 01 - MPG data set.srt | 1.9 KB |
| 02 - The regression tree prebuilt example.mp4 | 12.0 MB |
| 02 - The regression tree prebuilt example.srt | 6.3 KB |
| 03 - The math behind regression trees.mp4 | 4.0 MB |
| 03 - The math behind regression trees.srt | 3.6 KB |
| 04 - How RT handles nominal variables.mp4 | 11.1 MB |
| 04 - How RT handles nominal variables.srt | 6.5 KB |
| 05 - Ordinal variable handling.mp4 | 10.1 MB |
| 05 - Ordinal variable handling.srt | 5.4 KB |
| 06 - Closer look at a full regression tree.mp4 | 9.1 MB |
| 06 - Closer look at a full regression tree.srt | 5.3 KB |
| 07 - KNIME's missing data options for regression trees.mp4 | 7.7 MB |
| 07 - KNIME's missing data options for regression trees.srt | 4.5 KB |
| 08 - Line plot.mp4 | 7.9 MB |
| 08 - Line plot.srt | 3.8 KB |
| 09 - Accuracy.mp4 | 6.6 MB |
| 09 - Accuracy.srt | 3.6 KB |
| 01 - Next steps.mp4 | 1.7 MB |
| 01 - Next steps.srt | 1.6 KB |
| Bonus Resources.txt | 386 bytes |
| Chapter_2_Example_for_Learning_a_Decision_Tree.knwf | 787.2 KB |
| Chapter_3_Example_for_Learning_a_Decision_Tree.knwf | 787.2 KB |
| Chapter_4_Example_for_Learning_a_Decision_Tree.knwf | 787.2 KB |
Name
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Uploader
Size
S/L
Added
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360.5 MB
[2
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2023-10-24
| Uploaded by freecoursewb | Size 360.5 MB | Health [ 2 /1 ] | Added 2023-10-24 |
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713.3 MB
[3
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2023-10-24
| Uploaded by freecoursewb | Size 713.3 MB | Health [ 3 /2 ] | Added 2023-10-24 |
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311.0 MB
[11
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2023-10-23
| Uploaded by freecoursewb | Size 311.0 MB | Health [ 11 /3 ] | Added 2023-10-23 |
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341.7 MB
[14
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2023-06-01
| Uploaded by freecoursewb | Size 341.7 MB | Health [ 14 /11 ] | Added 2023-06-01 |
NOTE
SOURCE: Linkedin Machine Learning and AI Foundations Decision Trees with KNIME
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COVER
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MEDIAINFO
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