REFERENZEN

NATURAL LANGUAGE PROCESSING (NLP)

Branche: Marketing - Unternehmensgröße: <300

Projektinhalt:

Building a prescriptive model for advertisement text (with revenue uplift 7%) – conducted feasibility study for user recommendations based on recurrent neural networks.

Technologien & Methodik:

Python, TensorFlow, RNN, Machine & Deep Learning

Branche: Marketing - Unternehmensgröße: <300

Projektinhalt:

Text Mining: implementation of a new, scalable item based collaborative filtering algorithm for 150 million events per day.

Increase in click through rate by 10% compared to previous implementation – constructed real time streaming pipeline to transform newspaper text into vector format for about 20 texts per second being transformed, inserted and updated each day, implemented a fully automated model retraining pipeline.

Technologien & Methodik:

Python, Spark, TensorFlow, Machine & Deep Learning, Big Data

Branche: Banken - Unternehmensgröße: <1.200

Projektinhalt:

Design and implement a Data Science Stack for Text Mining.

Technologien & Methodik:

Regression, Classification and Apache Spark, Recurrent Neural Networks, Keras, Hadoop

Branche: Medien - Unternehmensgröße: <5.500

Projektinhalt:

Topic modeling: Use LDA-Algorithm to model the topics of German news
Doc2Vec: Use the topic model to transform each document to a topic vector
Similar documents detection: Use the topic model to transform the corpus to a topic matrix and search similar documents using the corpus-topic matrix
Keyword identification: Build a keyword extractor to identify keywords from German news

Technologien & Methodik:

Python, Gitlab, Jmeter, SonarQube, Docker, Rancher

Branche: E-Commerce - Unternehmensgröße: <1.000

Projektinhalt:

Analyze the text of product names, descriptions, etc. to improve search quality, develop novel POS and entity tagger and build a prediction model of the product categories by training a feedforward neural network.

Technologien & Methodik:

Python, NLTK, Sklearn, machine learning, Neural Networks

Branche: Versicherung - Unternehmensgröße: <1.200

Projektinhalt:

Web-mining & Text-analytics: crawl websites, apply text analytics techniques to extract information.
Natural language processing for German: POS-tagging and stemming based on statistical inference; topic and sentiment analysis of the news.

Technologien & Methodik:

R, R-Shiny, IBM Watson