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Regression Machine Learning with R

Last Update: July, 2018

1. Course Objective

Learn Regression Machine Learning main topics using R statistical software® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Create target and predictor algorithm features (supervised regression machine learning task).
  • Select relevant predictor features subset through univariate filter methods (Student’s t-test, ANOVA F-test), deterministic wrapper method (recursive feature elimination) and embedded method (least absolute shrinkage and selection operator LASSO).
  • Extract predictor features transformations (principal component analysis PCA).
  • Train algorithms such as generalized linear models GLM (linear regression, elastic net regression), similarity methods (k nearest neighbors KNN), frequency methods (decision tree), ensemble methods (random forest, gradient boosting machine GBM), maximum margin methods (support vector machine SVM), multi-layer perceptron methods (artificial neural networks ANN) and estimate their optimal parameters through time series cross-validation.
  • Test algorithms forecasting accuracy (mean absolute error, mean squared error, root mean squared error, mean absolute percentage error).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Academic researcher who wishes to deepen your knowledge in data mining, applied statistical learning or artificial intelligence.
  • Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.
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