Aim of the project
The main goal of this project is to prepare an intuitive discussion of popular machine learning algorithms (such as linear regression, logistic regression, k-nearest neighbors method, LASSO regulated regression and flexible networks, support vector machine, decision and regression trees, bagging and boosting, including forests and xgboost algorithm, neural networks, combining models – e.g. stacking) and practical tutorials showing their use in various programming languages (including R, python, julia, scala, ruby). An additional goal is to compare the performance of these algorithms and the results obtained in different programming languages.
The main goal of this project is to prepare an intuitive discussion of popular machine learning algorithms (…) in various programming languages (including R, python, julia, scala, ruby).
Thanks to the efficiency and ease of application, even without a deep understanding, machine learning algorithms have become a popular tool in many industries, where quality of prediction counts, even without the need to understand the relationship connecting the analyzed variables. Understanding the basics of the algorithm used and its adaptation (e.g. selection of appropriate hyperparameters) to a specific problem can often further improve the results obtained.