This is a hands-on based on a Databricks community cloud notebook using the Breeze library for linear algebra in Scala and the Spark MLlib library for distributed machine learning. The objective of this hands-on is to, first, get you started with linear algebra in Scala and, second, apply these operations in order to implement basic machine learning methods, namely principal component and regularized regression analysis both directly in Breeze and using MLlib. In the last part of the notebook, I introduce parameter tuning using the MLlib tuning package.
In order to make use of this notebook, you have to register at the free-of-charge Databricks community cloud and open the notebook using this URL. Alternatively, you may browse the html version of the notebook and replicate those parts of the hands-on which do not depend on Databricks' data sets. Currently, you can not directly display the html file from GitHub. Instead, you have to clone this repository and open the local html file with your browser.
ScalaLinearAlgebraHandsOn.html: The notebook in html format.
ScalaLinearAlgebraHandsOn.scala: The content of the notebook as Scala source code. All non-Scala notebook cells were converted to comments.