Course Description
John Wiley & Sons, Inc. USA
Course curriculum

1
6.1 Introduction to Machine Learning

6.1.1 Accuracy Measures Using R

6.1.2 Understanding Machine Learning Technology Part1

6.1.3 Understanding Machine Learning Technology Part2

6.1.4 Understanding Machine Learning Technology Part3

6.1.5 Understanding Machine Learning technology Part4

P.6.1 Introduction to Machine Learning Part 1

P.6.1 Introduction to Machine Learning Part 2


2
6.2 Graphical Models and Bayesian Networks

6.2.1 Graphical Models and Bayesian Networks on R Part1

6.2.2 Graphical Models and Bayesian Networks on R Part2

6.2.3 Graphical Models and Bayesian Networks on R Part3

6.2.4 Graphical Models and Bayesian Networks on R Part4

6.2.5 Graphical Models and Bayesian Networks on R Part5

P.6.2 Graphical Models and Bayesian Networks Part 1

P.6.2 Graphical Models and Bayesian Networks Part 2

P.6.2 Graphical Models and Bayesian Networks Part 3


3
6.3 Artificial Neural Networks

6.3.1 Artificial Neural Networks Part1

6.3.2 Artificial Neural Networks Part2

6.3.3 Artificial Neural Networks Part3

6.3.4 Artificial Neural Networks Part4

P.6.3 Artificial Neural Networks Part 1

P.6.3 Artificial Neural Networks Part 2

P.6.3 Artificial Neural Networks Part 3


4
6.4 Dimensionality Reduction Using PCA and Factor Analysis on R

6.4.1 Performing Dimensionality Reduction

6.4.2 Dimensionaluty Reduction Using PCA Part2

6.4.3 Dimensionaluty Reduction Using PCA Part3

P.6.4 Dimensionality Reduction Using PCA and Factor Analysis on R Part 1

P.6.4 Dimensionality Reduction Using PCA and Factor Analysis on R Part 2


5
6.5 Support Vector Machines

6.5.1 Support vector machines Part1

6.5.2 Support Vector Machines Part2

6.5.3 Churn with Support Vector Machines

P.6.5 Support Vector Machines Part 1

P.6.5 Support Vector Machines Part 2
