Lessons: 20Length: 2.8 hours
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?
1.Introduction
1.1Introduction09:36
1.1
Introduction
09:36
1.2Machine Learning Basics09:00
1.2
Machine Learning Basics
09:00
2.Supervised Learning
2.1Supervised Learning Summary03:52
2.1
Supervised Learning Summary
03:52
2.2k-Nearest Neighbor08:37
2.2
k-Nearest Neighbor
08:37
2.3Decision Trees11:47
2.3
Decision Trees
11:47
2.4Perceptrons08:08
2.4
Perceptrons
08:08
2.5Linear Regression10:01
2.5
Linear Regression
10:01
2.6Naive Bayesian Classifiers06:57
2.6
Naive Bayesian Classifiers
06:57
2.7General Regression Neural Networks07:08
2.7
General Regression Neural Networks
07:08
2.8Feed-Forward Neural Networks14:19
2.8
Feed-Forward Neural Networks
14:19
2.9Support Vector Machines14:27
2.9
Support Vector Machines
14:27
2.10Random Forests07:56
2.10
Random Forests
07:56
3.Unsupervised Learning
3.1Unsupervised Learning Summary04:54
3.1
Unsupervised Learning Summary
04:54
3.2k-Means Clustering07:45
3.2
k-Means Clustering
07:45
3.3Hierarchical Clustering07:35
3.3
Hierarchical Clustering
07:35
3.4Self-Organizing Maps08:36
3.4
Self-Organizing Maps
08:36
3.5Apriori Association08:55
3.5
Apriori Association
08:55
4.Theory & Practice
4.1Theory12:10
4.1
Theory
12:10
4.2Practice05:50
4.2
Practice
05:50
5.Conclusion
5.1Wrap Up01:33
5.1
Wrap Up
01:33
