Course Description

John Wiley & Sons, Inc. USA
Course curriculum
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6.1.1 Accuracy Measures Using R
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6.1.2 Understanding Machine Learning Technology Part1
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6.1.3 Understanding Machine Learning Technology Part-2
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6.1.4 Understanding Machine Learning Technology Part-3
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6.1.5 Understanding Machine Learning technology Part-4
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P.6.1 Introduction to Machine Learning Part-1
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P.6.1 Introduction to Machine Learning Part-2
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6.2.1 Graphical Models and Bayesian Networks on R Part-1
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6.2.2 Graphical Models and Bayesian Networks on R Part-2
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6.2.3 Graphical Models and Bayesian Networks on R Part-3
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6.2.4 Graphical Models and Bayesian Networks on R Part-4
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6.2.5 Graphical Models and Bayesian Networks on R Part-5
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P.6.2 Graphical Model and Bayesian Networks Part-1
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P.6.2 Graphical Model and Bayesian Networks Part-2
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P.6.2 Graphical Model and Bayesian Networks Part-3
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6.3.1 Artificial Neural Networks Part-1
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6.3.2 Artificial Neural Networks Part-2
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6.3.3 Artificial Neural Networks Part-3
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6.3.4 Artificial Neural Networks Part-4
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P.6.3 Artificial Neural Networks Part-1
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P.6.3 Artificial Neural Networks Part-2
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P.6.3 Artificial Neural Networks Part-3
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6.4.1 Performing Dimensionality Reduction
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6.4.2 Dimensionaluty Reduction Using PCA Part-2
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6.4.3 Dimensionaluty Reduction Using PCA Part-3
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P.6.4 Dimensionality Reduction using PCA and Factor Analysis on R Part-1
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P.6.4 Dimensionality Reduction using PCA and Factor Analysis on R Part-2
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6.5.1 Support Vector Machines Part-1
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6.5.2 Support Vector Machines Part-2
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6.5.3 Churn with Support Vector Machines
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P.6.5 Support Vector Machines Part-2
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P.6.5 Support Vector Machines Part-1
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About this course
- Free
- 32 lessons
- 3.5 hours of video content