We offer Big Data Analysis based on mathematical data science methods such as statistical analysis and classification, machine based learning as well as deep learning methods with neural networks.

We are trained to analyse large amounts of multidimensional, scalar & non-scalar, non-normalised and anonymised customer data sets. We deliver added value and understanding to your data, support your business decisions, find evidence to hypothesis and help automate your human expert activities.

We use state of the art mathematical methods and engineer product agnostic solutions with open source libraries of programming languages such as Python or R. To significantly reduce computing time on large data sets we offer cloud based super computing services with GPU enhanced parallel computing clusters.

Further we offer support in using cloud based machine learning product & API usage & support with

• Google Machine Learning Platform
• Facebooks Scalable Deep Learning Framework
• Apple Core ML Framework

Offered Methods & Models

All methods include preprocessing of incomplete, non-normalised data sets in our offer. If wished we also support our customers in extracting and anonymising the data from their data stores such as SQL and No-SQL Databases or Business Intelligence Systems.

Regression Analysis

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression

Classification Analysis

• Logistic Regression
• K-Nearest Neighbours (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM (eg. Gaussian RBF, Sigmoid, Polynomial )
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

Clustering Analysis

• K-Means Clustering
• Hierarchical Clustering

Association Rule Learning

• Apriori
• Eclat

Reinforcement Learning

• Upper Confidence Bound (UCB)
• Thompson Sampling

Natural Language Processing

• Chat-Bots

Unsupervised Deep Learning

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN with LSTM)

Supervised Deep Learning

• Self-Organizing Maps
• Deep Boltzmann Machines
• AutoEncoders

Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA

Model Selection & Boosting

• k-Fold Cross Validation
• Grid Search
• XGBoost

Stochastic Process Models

• Markov Chains & HMM
• Kalman Filter
• Queueing Theories
• Monte Carlo Simulation

Do You accumulate exabytes of data and get the impression that you build a haystack around your needle?

eXception handler offers state of the art, product agnostic, Data Analysis, Statistics and Machine Learning solutions for your data. Let us find added value in your data and ask us for availability and rates.

Fancy for more? Check the blog videos on AI