FREELessons: 20Length: 2.8 hours
- Overview
- Transcript
2.5 Linear Regression
Linear regression is a model that uses a weighted sum of the inputs to compute the output. This method utilizes an analytical solution to learn the optimal values for the weights.
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
