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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