| 01 - Introducing Leo Breiman and CART.mp4 | 11.6 MB | ||
| 01 - Introducing Leo Breiman and CART.srt | 5.9 KB | ||
| 01 - MPG data set.mp4 | 4.5 MB | ||
| 01 - MPG data set.srt | 1.9 KB | ||
| 01 - Next steps.mp4 | 1.7 MB | ||
| 01 - Next steps.srt | 1.6 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 | ||
| 01 - The basics of decision trees.mp4 | 7.2 MB | ||
| 01 - The basics of decision trees.srt | 2.1 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 | ||
| 02 - The regression tree prebuilt example.mp4 | 12 MB | ||
| 02 - The regression tree prebuilt example.srt | 6.3 KB | ||
| 02 - Understanding the entropy calculation.mp4 | 11.7 MB | ||
| 02 - Understanding the entropy calculation.srt | 9.1 KB | ||
| 02 - What is the Gini coefficient.mp4 | 7 MB | ||
| 02 - What is the Gini coefficient.srt | 4 KB | ||
| 02 - What you should know.mp4 | 2 MB | ||
| 02 - What you should know.srt | 1.6 KB | ||
| 03 - How C4.5 handles missing data.mp4 | 6 MB | ||
| 03 - How C4.5 handles missing data.srt | 4.4 KB | ||
| 03 - How CART handles missing data using surrogates.mp4 | 9.8 MB | ||
| 03 - How CART handles missing data using surrogates.srt | 8 KB | ||
| 03 - How to use the practice files.mp4 | 4.5 MB | ||
| 03 - How to use the practice files.srt | 2.2 KB | ||
| 03 - Introducing KNIME.mp4 | 12.8 MB | ||
| 03 - Introducing KNIME.srt | 6 KB | ||
| 03 - The math behind regression trees.mp4 | 4 MB | ||
| 03 - The math behind regression trees.srt | 3.6 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 | ||
| 04 - Changing the settings in KNIME.mp4 | 7.8 MB | ||
| 04 - Changing the settings in KNIME.srt | 4.5 KB | ||
| 04 - How RT handles nominal variables.mp4 | 11.1 MB | ||
| 04 - How RT handles nominal variables.srt | 6.5 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 - An overview of decision tree algorithms.mp4 | 12.5 MB | ||
| 05 - An overview of decision tree algorithms.srt | 5.8 KB | ||
| 05 - How CART handles nominal variables.mp4 | 4.6 MB | ||
| 05 - How CART handles nominal variables.srt | 2.6 KB | ||
| 05 - Ordinal variable handling.mp4 | 10.1 MB | ||
| 05 - Ordinal variable handling.srt | 5.4 KB | ||
| 05 - Working with the prebuilt example.mp4 | 15.9 MB | ||
| 05 - Working with the prebuilt example.srt | 8.8 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 | ||
| 06 - Closer look at a full regression tree.mp4 | 9.1 MB | ||
| 06 - Closer look at a full regression tree.srt | 5.3 KB | ||
| 06 - KNIME settings for C4.5.mp4 | 8.6 MB | ||
| 06 - KNIME settings for C4.5.srt | 4.9 KB | ||
| 07 - Evaluating the accuracy of your CART tree.mp4 | 3.4 MB | ||
| 07 - Evaluating the accuracy of your CART tree.srt | 2 KB | ||
| 07 - How C4.5 handles nominal variables.mp4 | 7.4 MB | ||
| 07 - How C4.5 handles nominal variables.srt | 3.6 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 - How C4.5 handles continuous variables.mp4 | 4.2 MB | ||
| 08 - How C4.5 handles continuous variables.srt | 2.3 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 | ||
| 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 | ||
| Bonus Resources.txt | 409.6 B | ||
| 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 | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 79 total files | |||
Machine Learning and AI Foundations: Decision Trees with KNIME
https://FreeCourseWeb.com
Released 06/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 59m | Size: 311 MB
Many data science specialists are looking to pivot toward focusing on machine learning. In this course, Keith McCormick covers the essentials of machine learning pertaining to predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. nstrations of using the KNIME modeler are included so you can understand how decision trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
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[LinkedIn Learning] Advance Your Skills as a Machine Learning Specialist - Complete 9 Courses Posted by
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[LinkedIn Learning] Getting Started with AI and Machine Learning - Complete 7 Courses Posted by
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