Machine Learning Distilled

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Welcome to Machine Learning Distilled. In this course, Kenan Casey reviews machine learning, and takes you through some important concepts, distilled. In other words, what is machine learning, why would you want to do it, and how is it done?

Free Preview: Machine Learning Distilled

Introduction

09:36

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


Preview for Machine Learning Distilled
Lessons:
20
Length:
2.8 hours
Tagged with:
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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