Applied Machine Learning in R
Get the essential machine learning skills and use them in real life situations
What you’ll learn
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Understand the essential concepts related to machine learning
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Perform model cross-validation to assess model stability on independent data sets
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Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
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Perform logistic regression and discriminant analysis
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Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
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Use neural networks to make predictions
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Use principal components analysis to detect patterns in variables
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Conduct cluster analysis to group observations into homogeneous classes
Requirements
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Knowledge of the R program
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Basic knowledge of statistics and statistical analysis
Who this course is for:
- Data analysts
- Data scientists
- Researchers
- Students
