Kamel Jebreen

I am R&D engineer specialized in machine learning, big data problems, and graphical models (Bayesian networks and dynamic Bayesian networks) for classification, and time-series data. 1. DATA SCIENCE: Supervised learning for regressions and classification (KNN, CART, Random Forest, Bagging, Stacking, SVM, Bayesian networks, graphical models, linear and logistic regression, data visualization (feature selection, discretization, PCA)) and Unsupervised learning (clustering (K-means, CART) and interfere dynamic interaction and casual networks). 2. MATHEMATICS: Theory of machine learning, the theory of applied statics, measure probability theory, and numerical analysis. 3. BIG DATA: Manipulate high dimensional data with nonparametric approaches (LASSO, Random Forest and SVM) to perform high accuracy in regression, classification or interfering the intercalation and casual networks. 4. Computer Languages: R, Python, MATLAB, SQL, C, and C++.