(3) Ion mobility. Ion mobility is a technique to separate ionized analytes based on their size, shape, and physio-chemical properties. Initially the techniques for ion mobility propelled the ions with an electric field through a cell with inert gas where the ions collide with the inert gas without fragmentation. Separation is then achieved by the ions traveling faster or slower in the electric field (i.e., based on their charge) through the collisions with the gas (i.e., based on shape and size). Traveling wave ion mobility (TWIMS) works on the same principle but pushes the ions forward through the ion mobility cell with a wave of electric field. Trapped ion mobility (TIMS) reverses this operation by trapping the ions in an electric field and forcing them forward by collision with the gas. From any of the different ion mobility techniques one can derive the collisional crosssection (CCS) in Ångströms squared with the use of calibration analytes that have a known CCS. Historically most methods were based on molecular dynamics models that calculate the CCS from first principles in physics. Lately the field has published multiple ML and DL approaches for both peptide and metabolite CCS prediction. The tutorials made available in ProteomicsML use both TIMS and TWIMS data, where the large TIMS data set is from Meiers et al. (718,917 data points) and the TWIMS data is from Puyvelde et al. (6268 data points). The tutorial is a walkthrough that trains linear models to more complex non-linear models (e.g., DL based networks) showing advantages and disadvantages of the learning algorithms for CCS prediction.