Regression Machine Learning with R

Learn at your own pace with over 40 lectures and more than 5.5 hours of content.

At the end of this course you will be able to:

  • Evaluate model bias-variance trade-off which can possibly lead to its under-fitting or over-fitting.
  • Assess goodness-of-fit and test forecasting accuracy with scale dependent and scale-independent metrics.
  • Calculate generalized linear models such as linear and elastic net regressions.
  • Analyze similarity methods such as k nearest neighbors’ regression and frequency methods such as decision tree regression.
  • Explore ensemble methods such as random forest and gradient boosting machine regression to advance decision tree prediction accuracy.
  • Estimate maximum margin methods such as support vector machines with linear and non-linear kernels.
  • Analyze multi-layer perceptron methods such as artificial neural networks.

This course is best for you as:

  • Student at any knowledge level who wants to learn 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 these concepts in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.

The requirements for this course are:

  • R statistical software is required. Downloading instructions included.
  • RStudio Integrated Development Environment is recommended. Downloading instructions included.
  • R script files provided with course.
  • Familiarity with software is recommended.
  • Mathematical formulae kept at minimum essential level for main concepts understanding.

Feel free to take a look at course curriculum here!