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Machine Learning Basics

09:00

Machine Learning Basics

In this lesson I'm going to define some basic machine learning terms and give an overview of supervised and unsupervised learning. I'm also going to introduce the fundamental tradeoff of machine learning.

1.Introduction
2 lessons, 18:36

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1.1
Introduction
09:36

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1.2
Machine Learning Basics
09:00

2.Supervised Learning
10 lessons, 1:33:12

2.1
Supervised Learning Summary
03:52

2.2
k-Nearest Neighbor
08:37

2.3
Decision Trees
11:47

2.4
Perceptrons
08:08

2.5
Linear Regression
10:01

2.6
Naive Bayesian Classifiers
06:57

2.7
General Regression Neural Networks
07:08

2.8
Feed-Forward Neural Networks
14:19

2.9
Support Vector Machines
14:27

2.10
Random Forests
07:56

3.Unsupervised Learning
5 lessons, 37:45

3.1
Unsupervised Learning Summary
04:54

3.2
k-Means Clustering
07:45

3.3
Hierarchical Clustering
07:35

3.4
Self-Organizing Maps
08:36

3.5
Apriori Association
08:55

4.Theory & Practice
2 lessons, 18:00

4.1
Theory
12:10

4.2
Practice
05:50

5.Conclusion
1 lesson, 01:33

5.1
Wrap Up
01:33


About Kenan Casey
Kenan Casey holds a Masters degree and Ph.D. in Computer Science, and is now an Assistant Professor at Freed-Hardeman University.
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