English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 59m | 323 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. Demonstrations 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.
Table of Contents
Introduction
1 The basics of decision trees
2 What you should know
3 How to use the practice files
Introducing Decision Trees
4 What is a decision tree
5 The pros and cons of decision trees
6 Introducing KNIME
7 A quick review of machine learning basics with examples
8 An overview of decision tree algorithms
Introducing the C5.0 Algorithm
9 Ross Quinlan, ID3, C4.5, and C5.0
10 Understanding the entropy calculation
11 How C4.5 handles missing data
12 The Give Me Some Credit data set
13 Working with the prebuilt example
14 KNIME settings for C4.5
15 How C4.5 handles nominal variables
16 How C4.5 handles continuous variables
17 Equal size sampling
18 A quick look at the complete C4.5 tree
19 Evaluating the accuracy of your C4.5 tree
20 When to turn off pruning
Introducing Classification Trees
21 Introducing Leo Breiman and CART
22 What is the Gini coefficient
23 How CART handles missing data using surrogates
24 Changing the settings in KNIME
25 How CART handles nominal variables
26 A quick look at the complete CART tree
27 Evaluating the accuracy of your CART tree
Introducing Regression Trees
28 MPG data set
29 The regression tree prebuilt example
30 The math behind regression trees
31 How RT handles nominal variables
32 Ordinal variable handling
33 Closer look at a full regression tree
34 KNIME’s missing data options for regression trees
35 Line plot
36 Accuracy
Conclusion
37 Next steps
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