Machine Learning is an extremely popular topic within the field of Artificial Intelligence. We encounter the results of machine learning algorithms daily, for example, when we play online games or do online shopping to applying for an insurance or “driving” a driver-less car.

One way to define machine learning is the intersection between statistics and computer science. The R programming language is perfectly positioned to handle both fields extremely well and in an efficient and powerful way. It offers a huge variety of statistical analysis solutions with over 18000 packages which include a wide array of machine learning implementations. For example, one can apply a Boosting and Gradient Descent algorithm, build a Random Forest model, or design a Neural Network and much more.

This course is structured in the following main parts:

·         Prepare the data

·         Define the problem

·         Design machine learning workflow

·         Explore available algorithms

·         Explore R packages for ML

·         Cross-validation

·         Model fitting and hyper parameter tuning

·         Evaluate model performance