Freienbach, Schwyz, Switzerland
Areas of expertise: A. Machine Learning: - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression - Classification: Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification - Clustering: K-Means, Hierarchical Clustering - Association Rule Learning: Apriori, Eclat - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling - Natural Language Processing: Bag-of-words model and algorithms for NLP - Dimensionality Reduction: Principal Component Analysis, Linear Discriminant Analysis, Kernel PCA - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost, CatBoost B. Deep learning: - Artificial Neural Networks to solve Customer Churn problems - Convolutional Neural Networks for Image Recognition - Recurrent Neural Networks to predict Stock Prices - Self-Organizing Maps to investigate Credit Card Fraud - Boltzmann Machines to create Binary Recommender Systems - Stacked Autoencoders to create Multiple Category Recommender Systems C. Programming Languages & Libraries: - Python, Tensorflow, Pytorch, Keras, Scikit-learn, Numpy, Matplotlib, Pandas D. Tools: - Spyder, Anaconda, Jupyter Notebook, Google Colaboratory