1.Introduction

2 lessons, 18:36

1.1

Introduction

09:36

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