Last week, at Bucharest FP Meetup, I made a general introduction to machine learning and R programming language. The first part was about R, then I presented some problems that might appear when one applies machine learning on data without looking at the data or how the data was obtained. The last part was about linear regression and random forest. The slides can be downloaded from Github and this is the abstract:

ABSTRACT: This presentation will first provide an overview on some basic machine learning concepts like the bias-variance tradeoff, the curse of dimensionality, overfitting and cross-validation. We will then take a deeper dive into the bias-variance tradeoff and take a look at some machine learning algorithms from both ends of the spectrum, like linear regression and ensemble methods.