Udemy - Fundamentals of Decision Trees in Machine Learning [Course Drive]
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Files
| 001 Welcome.mp4 | 13.3 MB | ||
| 002 Introduction.mp4 | 20.9 MB | ||
| 003 Ex-Files-MachineLearning-Trees.zip | 76.1 KB | ||
| 003 Getting started.mp4 | 20.8 MB | ||
| 004 Decision tree options in SPSS Modeler.mp4 | 30.3 MB | ||
| 005 Building CHAID model and add a second model with CRT.mp4 | 64.1 MB | ||
| 006 Analysis nodes.mp4 | 26.8 MB | ||
| 007 Lift and gains chart.mp4 | 38.8 MB | ||
| 008 Whats an algorithm.mp4 | 17 MB | ||
| 009 Chi-squared.mp4 | 28.8 MB | ||
| 010 Buliding a tree interactively.mp4 | 30.2 MB | ||
| 011 Bonferonni adjustment and level of measurement.mp4 | 64.6 MB | ||
| 012 CHAID.mp4 | 92.4 MB | ||
| 013 Gini coefficient.mp4 | 21.3 MB | ||
| 014 Understanding CRT.mp4 | 84 MB | ||
| 015 The complete CRT tree.mp4 | 26.6 MB | ||
| 016 Stopping rules in CHAID and CRT.mp4 | 47.2 MB | ||
| 017 Improving your model.mp4 | 61 MB | ||
| 018 Ex-Files-ML-Adv-DecisionTrees.zip | 873.3 KB | ||
| 018 Understanding QUEST.mp4 | 6.6 MB | ||
| 019 How QUEST handles variables.mp4 | 72.2 MB | ||
| 020 How QUEST handles missing data.mp4 | 26.5 MB | ||
| 021 Pruning and stopping rules in QUEST.mp4 | 44.9 MB | ||
| 022 ID3 and C4.5.mp4 | 16.1 MB | ||
| 023 Winnowing attributes and rule sets.mp4 | 63.1 MB | ||
| 024 Understanding information gain.mp4 | 24.9 MB | ||
| 025 Pruning in C5.0.mp4 | 77.4 MB | ||
| 026 How C5.0 handles missing data.mp4 | 15.3 MB | ||
| 027 Ensembles.mp4 | 61.7 MB | ||
| 028 Bagging.mp4 | 101.4 MB | ||
| 029 Random forests.mp4 | 28.1 MB | ||
| 030 Boosting.mp4 | 39.8 MB | ||
| 031 Costs and priors.mp4 | 51.3 MB | ||
| Course Downloaded from coursedrive.org.txt | 512 B | ||
| Must Read.txt | 512 B | ||
| ReadMe.txt | 512 B | ||
| Visit Coursedrive.org.url | 102.4 B | ||
| ▲ 41 total files | |||
Description
Fundamentals of Decision Trees in Machine Learning Download
Learn the fundamentals of trees in Machine learning
What you'll learn
• Learn the fundamentals of decision trees in machine learning
• Using the SPSS Modeler
• Building a CHAID model
• Using a lift and gains chart
• Exploring algorithms
• Building a tree interactively
Requirements
• Basic understanding of statistics
Description
A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees.
In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work.
We'll also explore advanced concepts and details of decision tree algorithms.
This course is designed to give you a solid foundation on which to build more advanced data science skills.
Who this course is for:
• Anyone interested in learning machine learning
• Data science specialists
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