# Visual Simulator for Mastering Fundamental Concepts of Machine Learning
## 2 Related Work and Requirements
### Analysis of Existing Solutions  
The presented analyses showed in which way machine learning courses are organized in prestigious universities in the world, as well as which tools and technologies are used for practical lessons. Programming languages Python and R are the most frequent technology choices for practical lessons due to their capacity to realize and visualize a large number of machine learning algorithms, but they are dependent on learners' programming experience. Some of these courses make it easier for students to program algorithms using web tools such as Jypiter Notebook, while others require advanced knowledge of programming and specific programming languages. As already stated, the focus of the analysis was the choice of the appropriate tool rather than the programming language. Among the tools, the best candidates are Weka, Alspace, JMP and Keel [28; 29; 30; 31; 32]. Weka is an open code software system developed at the University of Waikato in New Zealand, which is used in the area of machine learning [28]. The software can be used for machine learning model training and prediction initiation without the need to write any line of code. The tool can be used with a rich user interface or terminal and involves several methods for common machine learning tasks and additionally enables access to known tool frameworks such as scikit-learn, programming language R, and Deeplearning4j. The advantages of Weka are therefore the analysis and application of the entire process of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Course Title** & **Level of** **Study** & **University** & **Lab.** & **Tool or** **Exercises** & **Tool or** **Technology for** **Lab** & **Project** & **Tool or** **Own Tool** \\ \hline Introduction to & bachelor & University of British & + & Decision Trees & + & Seminar paper in & + \\ Artificial Intelligence & & Columbia & & [14] & + & Latex text editor & + \\ Introduction to & & University of Texas at & & + & Python, Mash [12] & + & Darwin & + \\ Data Mining & & & Austin & + & Keel, Weka & + & R & + \\ Sistemas Inteligentes & & & University of & + & Keel, Weka & + & R & + \\ para la Gestion de la Empresa & & & Granada & & & & & \\ Data Mining & & & University of & + & Weka & + & Weka & + \\ with Weka & & & Ecole Polytechnique & & & & & \\ Introduction to & & bachelor & Federale de & & & + & Python & \\ Machine Learning & & & Lausanne & & & & & \\  & & & Ecole Polytechnique & & & & & \\ Machine Learning & & & Federale de & & & + & Python & \\  & & & Lausanne & & & & & \\ Machine Learning: & & & Technical University & + & Python & & & \\ Methods and Tools & & & Wageningen & & & & & \\ Machine Learning & & & University \& + & Python & + & Python & \\ Machine learning for & & & University of & & & & + & Python \\ data science & & & Ljubljana & & & & \\ Machine Learning 1 & & & University of Zagreb & + & Python & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._machine learning, from data pre-processing, creating a model by choosing one of many realized machine learning algorithms, and making predictions based on input data. Many researchers have used this tool because of its intuitive user interface and easy way to get results from a trained model [33; 34; 35; 36; 37; 38]. The tool Keel is an open code software, developed at the University of Granada, Spain. It is written in the Java programming language, which encourages the user to understand the process of learning and techniques used in machine learning problems such as classification and regression [32]. The main characteristics of Keel are its very simple and intuitive graphic user interface, which makes the work easier for new users; its other advantages are the existence of numerous incorporated machine learning algorithms, the data pre-processing technique, and the comparison of performances and statistical elements of analysis.