Course Description

Machine Learning can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems.

John Wiley & Sons, Inc. USA

Established in 1807, Wiley is a global provider of content-enabled solutions to improve outcomes in research, education and professional practice with online tools, books, and eLearning trainings. Wiley’s strength lies in every major scientific, academic, and professional field.

Course curriculum

    1. 13.1.1 Accuracy Measures Using R

    2. 13.1.2 Understanding Machine Learning Technology Part-1

    3. 13.1.3 Understanding Machine Learning Technology Part-2

    4. 13.1.4 Understanding Machine Learning Technology Part-3

    5. 13.1.5 Understanding Machine Learning technology Part-4

    6. P.13.1 Introduction to Machine Learning Part-1

    7. P.13.1 Introduction to Machine Learning Part-2

    1. 13.2.1 Graphical Models and Bayesian Networks on R Part-1

    2. 13.2.2 Graphical Models and Bayesian Networks on R Part-2

    3. 13.2.3 Graphical Models and Bayesian Networks on R Part-3

    4. 13.2.4 Graphical Models and Bayesian Networks on R Part-4

    5. 13.2.5 Graphical Models and Bayesian Networks on R Part-5

    6. P.13.2 Graphical Model and Bayesian Networks Part-1

    7. P.13.2 Graphical Model and Bayesian Networks Part-2

    8. P.13.2 Graphical Model and Bayesian Networks Part-3

    1. 13.3.1 Artificial Neural Networks Part-1

    2. 13.3.2 Artificial Neural Networks Part-2

    3. 13.3.3 Artificial Neural Networks Part-3

    4. 13.3.4 Artificial Neural Networks Part-4

    5. P.13.3 Artificial Neural Networks Part-1

    6. P.13.3 Artificial Neural Networks Part-2

    7. P.13.3 Artificial Neural Networks Part-3

    1. 13.4.1 Performing Dimensionality Reduction

    2. 13.4.2 Dimensionaluty Reduction Using PCA Part-2

    3. 13.4.3 Dimensionaluty Reduction Using PCA Part-3

    4. P.13.4 Dimensionality Reduction using PCA and Factor Analysis on R Part-1

    5. P.13.4 Dimensionality Reduction using PCA and Factor Analysis on R Part-2

    1. 13.5.1 Support Vector Machines Part-1

    2. 13.5.2 Support Vector Machines Part-2

    3. 13.5.3 Churn with Support Vector Machines

    4. P.13.5 Support Vector Machines Part-1

    5. P.13.5 Support Vector Machines Part-2

About this course

  • Free
  • 32 lessons
  • 3.5 hours of video content